def test_pipeline(MODEL_NAME, dataset, device, verbose, out_dir): # Load models print('\n>> Loading models...') model_ls = load_model(out_dir, device=device, only_best=False, verbose=verbose, filter=lambda df: df[df['model'] == MODEL_NAME][df['dataset'] == dataset.name]) # Preparing dataset print('\n>> Preparing data...') if MODEL_NAME in ['GCN']: if model_ls[0]['net_params']['self_loop']: print("[!] Adding graph self-loops for GCN/GAT models (central node trick).") dataset._add_self_loops() testset = dataset.test print("Test Graphs: ", len(testset)) # Batching test data test_loader = DataLoader(testset, batch_size=model_ls[0]['net_params']['batch_size'], shuffle=False, drop_last=False, collate_fn=dataset.collate) # Test models print('\n>> Testing models...') acc_ls = [] for i, item in enumerate(model_ls): model = item['model'] net_params = item['net_params'] net_params['device'] = device # Set random seed set_random_seed(item['seed'], device) # Evaluate model _, test_acc = evaluate_network(model, device, test_loader, 0) acc_ls.append(test_acc) if verbose: print('\nModel #%s' % i) print('Test Accuracy: %s' % acc_ls[-1]) print('\n') print('AVG Test Accuracy: %s, s.d.: %s' % (np.mean(acc_ls), np.std(acc_ls)))
def train_val_pipeline(MODEL_NAME, dataset, params, net_params, dirs): start0 = time.time() per_epoch_time = [] DATASET_NAME = dataset.name if net_params['lap_pos_enc']: st = time.time() print("[!] Adding Laplacian positional encoding.") dataset._add_laplacian_positional_encodings(net_params['pos_enc_dim']) print('Time LapPE:', time.time() - st) if net_params['full_graph']: print("[!] Converting the given graphs to full graphs..") dataset._make_full_graph() print('Time taken to convert to full graphs:', time.time() - start0) if net_params['wl_pos_enc']: st = time.time() print("[!] Adding WL positional encoding.") dataset._add_wl_positional_encodings() print('Time WL PE:', time.time() - st) trainset, valset, testset = dataset.train, dataset.val, dataset.test root_log_dir, root_ckpt_dir, write_file_name, write_config_file = dirs device = net_params['device'] # Write network and optimization hyper-parameters in folder config/ with open(write_config_file + '.txt', 'w') as f: f.write( """Dataset: {},\nModel: {}\n\nparams={}\n\nnet_params={}\n\n\nTotal Parameters: {}\n\n""" .format(DATASET_NAME, MODEL_NAME, params, net_params, net_params['total_param'])) log_dir = os.path.join(root_log_dir, "RUN_" + str(0)) writer = SummaryWriter(log_dir=log_dir) # setting seeds random.seed(params['seed']) np.random.seed(params['seed']) torch.manual_seed(params['seed']) if device.type == 'cuda': torch.cuda.manual_seed(params['seed']) print("Training Graphs: ", len(trainset)) print("Validation Graphs: ", len(valset)) print("Test Graphs: ", len(testset)) print("Number of Classes: ", net_params['n_classes']) model = gnn_model(MODEL_NAME, net_params) model = model.to(device) optimizer = optim.Adam(model.parameters(), lr=params['init_lr'], weight_decay=params['weight_decay']) scheduler = optim.lr_scheduler.ReduceLROnPlateau( optimizer, mode='min', factor=params['lr_reduce_factor'], patience=params['lr_schedule_patience'], verbose=True) epoch_train_losses, epoch_val_losses = [], [] epoch_train_accs, epoch_val_accs = [], [] # import train and evaluate functions from train.train_SBMs_node_classification import train_epoch, evaluate_network train_loader = DataLoader(trainset, batch_size=params['batch_size'], shuffle=True, collate_fn=dataset.collate) val_loader = DataLoader(valset, batch_size=params['batch_size'], shuffle=False, collate_fn=dataset.collate) test_loader = DataLoader(testset, batch_size=params['batch_size'], shuffle=False, collate_fn=dataset.collate) # At any point you can hit Ctrl + C to break out of training early. try: with tqdm(range(params['epochs'])) as t: for epoch in t: t.set_description('Epoch %d' % epoch) start = time.time() epoch_train_loss, epoch_train_acc, optimizer = train_epoch( model, optimizer, device, train_loader, epoch) epoch_val_loss, epoch_val_acc = evaluate_network( model, device, val_loader, epoch) _, epoch_test_acc = evaluate_network(model, device, test_loader, epoch) epoch_train_losses.append(epoch_train_loss) epoch_val_losses.append(epoch_val_loss) epoch_train_accs.append(epoch_train_acc) epoch_val_accs.append(epoch_val_acc) writer.add_scalar('train/_loss', epoch_train_loss, epoch) writer.add_scalar('val/_loss', epoch_val_loss, epoch) writer.add_scalar('train/_acc', epoch_train_acc, epoch) writer.add_scalar('val/_acc', epoch_val_acc, epoch) writer.add_scalar('test/_acc', epoch_test_acc, epoch) writer.add_scalar('learning_rate', optimizer.param_groups[0]['lr'], epoch) t.set_postfix(time=time.time() - start, lr=optimizer.param_groups[0]['lr'], train_loss=epoch_train_loss, val_loss=epoch_val_loss, train_acc=epoch_train_acc, val_acc=epoch_val_acc, test_acc=epoch_test_acc) per_epoch_time.append(time.time() - start) # Saving checkpoint ckpt_dir = os.path.join(root_ckpt_dir, "RUN_") if not os.path.exists(ckpt_dir): os.makedirs(ckpt_dir) torch.save(model.state_dict(), '{}.pkl'.format(ckpt_dir + "/epoch_" + str(epoch))) files = glob.glob(ckpt_dir + '/*.pkl') for file in files: epoch_nb = file.split('_')[-1] epoch_nb = int(epoch_nb.split('.')[0]) if epoch_nb < epoch - 1: os.remove(file) scheduler.step(epoch_val_loss) if optimizer.param_groups[0]['lr'] < params['min_lr']: print("\n!! LR SMALLER OR EQUAL TO MIN LR THRESHOLD.") break # Stop training after params['max_time'] hours if time.time() - start0 > params['max_time'] * 3600: print('-' * 89) print( "Max_time for training elapsed {:.2f} hours, so stopping" .format(params['max_time'])) break except KeyboardInterrupt: print('-' * 89) print('Exiting from training early because of KeyboardInterrupt') _, test_acc = evaluate_network(model, device, test_loader, epoch) _, train_acc = evaluate_network(model, device, train_loader, epoch) print("Test Accuracy: {:.4f}".format(test_acc)) print("Train Accuracy: {:.4f}".format(train_acc)) print("Convergence Time (Epochs): {:.4f}".format(epoch)) print("TOTAL TIME TAKEN: {:.4f}s".format(time.time() - start0)) print("AVG TIME PER EPOCH: {:.4f}s".format(np.mean(per_epoch_time))) writer.close() """ Write the results in out_dir/results folder """ with open(write_file_name + '.txt', 'w') as f: f.write("""Dataset: {},\nModel: {}\n\nparams={}\n\nnet_params={}\n\n{}\n\nTotal Parameters: {}\n\n FINAL RESULTS\nTEST ACCURACY: {:.4f}\nTRAIN ACCURACY: {:.4f}\n\n Convergence Time (Epochs): {:.4f}\nTotal Time Taken: {:.4f} hrs\nAverage Time Per Epoch: {:.4f} s\n\n\n"""\ .format(DATASET_NAME, MODEL_NAME, params, net_params, model, net_params['total_param'], test_acc, train_acc, epoch, (time.time()-start0)/3600, np.mean(per_epoch_time)))
def train_val_pipeline(MODEL_NAME, dataset, params, net_params, dirs): start0 = time.time() per_epoch_time = [] DATASET_NAME = dataset.name if MODEL_NAME in ['GCN', 'GAT']: if net_params['self_loop']: print( "[!] Adding graph self-loops for GCN/GAT models (central node trick)." ) dataset._add_self_loops() if MODEL_NAME in ['GatedGCN_pyg', 'ResGatedGCN_pyg']: if net_params['pos_enc']: print("[!] Adding graph positional encoding.") dataset._add_positional_encodings(net_params['pos_enc_dim']) print('Time PE:', time.time() - start0) trainset, valset, testset = dataset.train, dataset.val, dataset.test # transform = T.ToSparseTensor() To do to save memory # self.train.graph_lists = [positional_encoding(g, pos_enc_dim, framework='pyg') for _, g in enumerate(dataset.train)] root_log_dir, root_ckpt_dir, write_file_name, write_config_file = dirs device = net_params['device'] # Write network and optimization hyper-parameters in folder config/ with open(write_config_file + '.txt', 'w') as f: f.write( """Dataset: {},\nModel: {}\n\nparams={}\n\nnet_params={}\n\n\nTotal Parameters: {}\n\n""" .format(DATASET_NAME, MODEL_NAME, params, net_params, net_params['total_param'])) log_dir = os.path.join(root_log_dir, "RUN_" + str(0)) writer = SummaryWriter(log_dir=log_dir) # setting seeds random.seed(params['seed']) np.random.seed(params['seed']) torch.manual_seed(params['seed']) if device.type == 'cuda': torch.cuda.manual_seed(params['seed']) print("Training Graphs: ", len(trainset)) print("Validation Graphs: ", len(valset)) print("Test Graphs: ", len(testset)) print("Number of Classes: ", net_params['n_classes']) model = gnn_model(MODEL_NAME, net_params) model = model.to(device) optimizer = optim.Adam(model.parameters(), lr=params['init_lr'], weight_decay=params['weight_decay']) scheduler = optim.lr_scheduler.ReduceLROnPlateau( optimizer, mode='min', factor=params['lr_reduce_factor'], patience=params['lr_schedule_patience'], verbose=True) epoch_train_losses, epoch_val_losses = [], [] epoch_train_accs, epoch_val_accs = [], [] if MODEL_NAME in ['RingGNN', '3WLGNN']: # import train functions specific for WL-GNNs from train.train_SBMs_node_classification import train_epoch_dense as train_epoch, evaluate_network_dense as evaluate_network train_loader = DataLoader(trainset, shuffle=True, collate_fn=dataset.collate_dense_gnn) val_loader = DataLoader(valset, shuffle=False, collate_fn=dataset.collate_dense_gnn) test_loader = DataLoader(testset, shuffle=False, collate_fn=dataset.collate_dense_gnn) else: # import train functions for all other GCNs from train.train_SBMs_node_classification import train_epoch_sparse as train_epoch, evaluate_network_sparse as evaluate_network # train_loader = DataLoaderpyg(trainset, batch_size=2, shuffle=False) train_loader = DataLoaderpyg( trainset, batch_size=params['batch_size'], shuffle=True) if params['framework'] == 'pyg' else DataLoader( trainset, batch_size=params['batch_size'], shuffle=True, collate_fn=dataset.collate) val_loader = DataLoaderpyg( valset, batch_size=params['batch_size'], shuffle=False) if params['framework'] == 'pyg' else DataLoader( valset, batch_size=params['batch_size'], shuffle=True, collate_fn=dataset.collate) test_loader = DataLoaderpyg( testset, batch_size=params['batch_size'], shuffle=False) if params['framework'] == 'pyg' else DataLoader( testset, batch_size=params['batch_size'], shuffle=True, collate_fn=dataset.collate) # At any point you can hit Ctrl + C to break out of training early. try: with tqdm(range(params['epochs']), ncols=0) as t: for epoch in t: t.set_description('Epoch %d' % epoch) start = time.time() if MODEL_NAME in [ 'RingGNN', '3WLGNN' ]: # since different batch training function for dense GNNs epoch_train_loss, epoch_train_acc, optimizer = train_epoch( model, optimizer, device, train_loader, epoch) else: # for all other models common train function epoch_train_loss, epoch_train_acc, optimizer = train_epoch( model, optimizer, device, train_loader, epoch, params['framework']) epoch_val_loss, epoch_val_acc = evaluate_network( model, device, val_loader, epoch, params['framework']) _, epoch_test_acc = evaluate_network(model, device, test_loader, epoch, params['framework']) epoch_train_losses.append(epoch_train_loss) epoch_val_losses.append(epoch_val_loss) epoch_train_accs.append(epoch_train_acc) epoch_val_accs.append(epoch_val_acc) writer.add_scalar('train/_loss', epoch_train_loss, epoch) writer.add_scalar('val/_loss', epoch_val_loss, epoch) writer.add_scalar('train/_acc', epoch_train_acc, epoch) writer.add_scalar('val/_acc', epoch_val_acc, epoch) writer.add_scalar('test/_acc', epoch_test_acc, epoch) writer.add_scalar('learning_rate', optimizer.param_groups[0]['lr'], epoch) t.set_postfix(time=time.time() - start, lr=optimizer.param_groups[0]['lr'], train_loss=epoch_train_loss, val_loss=epoch_val_loss, train_acc=epoch_train_acc, val_acc=epoch_val_acc, test_acc=epoch_test_acc) per_epoch_time.append(time.time() - start) # Saving checkpoint ckpt_dir = os.path.join(root_ckpt_dir, "RUN_") if not os.path.exists(ckpt_dir): os.makedirs(ckpt_dir) # the function to save the checkpoint # torch.save(model.state_dict(), '{}.pkl'.format(ckpt_dir + "/epoch_" + str(epoch))) files = glob.glob(ckpt_dir + '/*.pkl') for file in files: epoch_nb = file.split('_')[-1] epoch_nb = int(epoch_nb.split('.')[0]) if epoch_nb < epoch - 1: os.remove(file) scheduler.step(epoch_val_loss) # it used to test the scripts # if epoch == 1: # break if optimizer.param_groups[0]['lr'] < params['min_lr']: print("\n!! LR SMALLER OR EQUAL TO MIN LR THRESHOLD.") break # Stop training after params['max_time'] hours if time.time() - start0 > params['max_time'] * 3600: print('-' * 89) print( "Max_time for training elapsed {:.2f} hours, so stopping" .format(params['max_time'])) break except KeyboardInterrupt: print('-' * 89) print('Exiting from training early because of KeyboardInterrupt') _, test_acc = evaluate_network(model, device, test_loader, epoch, params['framework']) _, val_acc = evaluate_network(model, device, val_loader, epoch, params['framework']) _, train_acc = evaluate_network(model, device, train_loader, epoch, params['framework']) print("Test Accuracy: {:.4f}".format(test_acc)) print("Val Accuracy: {:.4f}".format(val_acc)) print("Train Accuracy: {:.4f}".format(train_acc)) print("Convergence Time (Epochs): {:.4f}".format(epoch)) print("TOTAL TIME TAKEN: {:.4f}s".format(time.time() - start0)) print("AVG TIME PER EPOCH: {:.4f}s".format(np.mean(per_epoch_time))) writer.close() """ Write the results in out_dir/results folder """ with open(write_file_name + '.txt', 'w') as f: f.write("""Dataset: {},\nModel: {}\n\nparams={}\n\nnet_params={}\n\n{}\n\nTotal Parameters: {}\n\n FINAL RESULTS\nTEST ACCURACY: {:.4f}\nval ACCURACY: {:.4f}\nTRAIN ACCURACY: {:.4f}\n\n Convergence Time (Epochs): {:.4f}\nTotal Time Taken: {:.4f} hrs\nAverage Time Per Epoch: {:.4f} s\n\n\n"""\ .format(DATASET_NAME, MODEL_NAME, params, net_params, model, net_params['total_param'], test_acc, val_acc,train_acc, epoch, (time.time()-start0)/3600, np.mean(per_epoch_time)))
def train_val_pipeline(MODEL_NAME, DATASET_NAME, params, net_params, args): # setting seeds random.seed(params['seed']) np.random.seed(params['seed']) torch.manual_seed(params['seed']) device = net_params['device'] if device == 'cuda': torch.cuda.manual_seed(params['seed']) dataset = LoadData(DATASET_NAME) trainset, valset, testset = dataset.train, dataset.val, dataset.test net_params['in_dim'] = torch.unique(dataset.train[0][0].ndata['feat'], dim=0).size( 0) # node_dim (feat is an integer) net_params['n_classes'] = torch.unique(dataset.train[0][1], dim=0).size(0) net_params['total_param'] = view_model_param(MODEL_NAME, net_params) load_model = args.load_model aug_type_list = [ 'drop_nodes', 'drop_add_edges', 'noise', 'mask', 'subgraph' ] if MODEL_NAME in ['GCN', 'GAT']: if net_params['self_loop']: print( "[!] Adding graph self-loops for GCN/GAT models (central node trick)." ) dataset._add_self_loops() print('-' * 40 + "Finetune Option" + '-' * 40) print('SEED: [{}]'.format(params['seed'])) print("Data Name: [{}]".format(DATASET_NAME)) print("Model Name: [{}]".format(MODEL_NAME)) print("Training Graphs:[{}]".format(len(trainset))) print("Valid Graphs: [{}]".format(len(valset))) print("Test Graphs: [{}]".format(len(testset))) print("Number Classes: [{}]".format(net_params['n_classes'])) print("Learning rate: [{}]".format(params['init_lr'])) print('-' * 40 + "Contrastive Option" + '-' * 40) print("Load model: [{}]".format(load_model)) print("Aug Type: [{}]".format(aug_type_list[args.aug])) print("Projection head:[{}]".format(args.head)) print('-' * 100) model = gnn_model(MODEL_NAME, net_params) if load_model: output_path = './001_contrastive_models' save_model_dir0 = os.path.join(output_path, DATASET_NAME) save_model_dir1 = os.path.join(save_model_dir0, aug_type_list[args.aug]) if args.head: save_model_dir1 += "_head" else: save_model_dir1 += "_no_head" save_model_dir2 = os.path.join(save_model_dir1, MODEL_NAME) load_file_name = glob.glob(save_model_dir2 + '/*.pkl') checkpoint = torch.load(load_file_name[-1]) model_dict = model.state_dict() state_dict = { k: v for k, v in checkpoint.items() if k in model_dict.keys() } model.load_state_dict(state_dict) print('Success load pre-trained model!: [{}]'.format( load_file_name[-1])) else: print('No model load!: Test baseline! ') model = model.to(device) optimizer = optim.Adam(model.parameters(), lr=params['init_lr'], weight_decay=params['weight_decay']) scheduler = optim.lr_scheduler.ReduceLROnPlateau( optimizer, mode='min', factor=params['lr_reduce_factor'], patience=params['lr_schedule_patience'], verbose=True) train_loader = DataLoader(trainset, batch_size=params['batch_size'], shuffle=True, collate_fn=dataset.collate) val_loader = DataLoader(valset, batch_size=params['batch_size'], shuffle=False, collate_fn=dataset.collate) test_loader = DataLoader(testset, batch_size=params['batch_size'], shuffle=False, collate_fn=dataset.collate) for epoch in range(params['epochs']): epoch_train_loss, epoch_train_acc, optimizer = train_epoch( model, optimizer, device, train_loader, epoch) epoch_val_loss, epoch_val_acc = evaluate_network( model, device, val_loader, epoch) epoch_test_loss, epoch_test_acc = evaluate_network( model, device, test_loader, epoch) _, epoch_test_acc = evaluate_network(model, device, test_loader, epoch) print('-' * 80) print( time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ' | ' + "Epoch [{:>2d}] Test Acc: [{:.4f}]".format( epoch + 1, epoch_test_acc)) print('-' * 80) scheduler.step(epoch_val_loss) if optimizer.param_groups[0]['lr'] < params['min_lr']: print("\n!! LR SMALLER OR EQUAL TO MIN LR THRESHOLD.") break _, test_acc = evaluate_network(model, device, test_loader, epoch) _, train_acc = evaluate_network(model, device, train_loader, epoch) print("Test Accuracy: {:.4f}".format(test_acc)) print("Train Accuracy: {:.4f}".format(train_acc)) return train_acc, test_acc
def train_val_pipeline(dataset, params, net_params): start0 = time.time() per_epoch_time = [] trainset, valset, testset = dataset.train, dataset.val, dataset.test device = net_params['device'] # setting seeds random.seed(params['seed']) np.random.seed(params['seed']) torch.manual_seed(params['seed']) if device.type == 'cuda': torch.cuda.manual_seed(params['seed']) print("Training Graphs: ", len(trainset)) print("Validation Graphs: ", len(valset)) print("Test Graphs: ", len(testset)) print("Number of Classes: ", net_params['n_classes']) model = DGNNet(net_params) model = model.to(device) optimizer = optim.Adam(model.parameters(), lr=params['init_lr'], weight_decay=params['weight_decay']) scheduler = optim.lr_scheduler.ReduceLROnPlateau( optimizer, mode='min', factor=params['lr_reduce_factor'], patience=params['lr_schedule_patience']) start_epoch = 0 epoch_train_losses, epoch_val_losses = [], [] epoch_train_accs, epoch_val_accs = [], [] train_loader = DataLoader(trainset, batch_size=params['batch_size'], shuffle=True, collate_fn=dataset.collate) val_loader = DataLoader(valset, batch_size=params['batch_size'], shuffle=False, collate_fn=dataset.collate) test_loader = DataLoader(testset, batch_size=params['batch_size'], shuffle=False, collate_fn=dataset.collate) # At any point you can hit Ctrl + C to break out of training early. try: with tqdm(range(start_epoch, params['epochs']), mininterval=params['print_epoch_interval'], maxinterval=None, unit='epoch', initial=start_epoch, total=params['epochs']) as t: for epoch in t: t.set_description('Epoch %d' % epoch) start = time.time() epoch_train_loss, epoch_train_acc, optimizer = train_epoch( model, optimizer, device, train_loader, epoch) epoch_val_loss, epoch_val_acc = evaluate_network( model, device, val_loader, epoch) _, epoch_test_acc = evaluate_network(model, device, test_loader, epoch) epoch_train_losses.append(epoch_train_loss) epoch_val_losses.append(epoch_val_loss) epoch_train_accs.append(epoch_train_acc) epoch_val_accs.append(epoch_val_acc) t.set_postfix(time=time.time() - start, lr=optimizer.param_groups[0]['lr'], train_loss=epoch_train_loss, val_loss=epoch_val_loss, train_acc=epoch_train_acc, val_acc=epoch_val_acc, test_acc=epoch_test_acc) per_epoch_time.append(time.time() - start) scheduler.step(epoch_val_loss) if optimizer.param_groups[0]['lr'] < params['min_lr']: print("\n!! LR SMALLER OR EQUAL TO MIN LR THRESHOLD.") break # Stop training after params['max_time'] hours if time.time() - start0 > params['max_time'] * 3600: print('-' * 89) print( "Max_time for training elapsed {:.2f} hours, so stopping" .format(params['max_time'])) break except KeyboardInterrupt: print('-' * 89) print('Exiting from training early because of KeyboardInterrupt') _, test_acc = evaluate_network(model, device, test_loader, epoch) _, val_acc = evaluate_network(model, device, val_loader, epoch) _, train_acc = evaluate_network(model, device, train_loader, epoch) print("Test Accuracy: {:.4f}".format(test_acc)) print("Train Accuracy: {:.4f}".format(train_acc)) print("Convergence Time (Epochs): {:.4f}".format(epoch)) print("TOTAL TIME TAKEN: {:.4f}s".format(time.time() - start0)) print("AVG TIME PER EPOCH: {:.4f}s".format(np.mean(per_epoch_time)))
def train_val_pipeline(MODEL_NAME, dataset, params, net_params, dirs): start0 = time.time() per_epoch_time = [] DATASET_NAME = dataset.name if MODEL_NAME in ['GCN', 'GAT']: if net_params['self_loop']: print("[!] Adding graph self-loops for GCN/GAT models (central node trick).") dataset._add_self_loops() if MODEL_NAME in ['GatedGCN']: if net_params['pos_enc']: print("[!] Adding graph positional encoding.") dataset._add_positional_encodings(net_params['pos_enc_dim']) print('Time PE:', time.time() - start0) trainset, valset, testset = dataset.train, dataset.val, dataset.test root_log_dir, root_ckpt_dir, write_file_name, write_config_file = dirs device = net_params['device'] # Write network and optimization hyper-parameters in folder config/ with open(write_config_file + '.txt', 'w') as f: f.write("""Dataset: {},\nModel: {}\n\nparams={}\n\nnet_params={}\n\n\nTotal Parameters: {}\n\n""".format( DATASET_NAME, MODEL_NAME, params, net_params, net_params['total_param'])) log_dir = os.path.join(root_log_dir, "RUN_" + str(0)) writer = SummaryWriter(log_dir=log_dir) # setting seeds random.seed(params['seed']) np.random.seed(params['seed']) torch.manual_seed(params['seed']) if device.type == 'cuda': torch.cuda.manual_seed(params['seed']) if hydra.is_first_execution(): print("Training Graphs: ", len(trainset)) print("Validation Graphs: ", len(valset)) print("Test Graphs: ", len(testset)) print("Number of Classes: ", net_params['n_classes']) model = EIGNet(net_params) model = model.to(device) optimizer = optim.Adam(model.parameters(), lr=params['init_lr'], weight_decay=params['weight_decay']) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=params['lr_reduce_factor'], patience=params['lr_schedule_patience'], verbose=True) if hydra.is_first_execution(): start_epoch = 0 else: start0 -= hydra.retrieved_checkpoint.time_elapsed start_epoch = hydra.retrieved_checkpoint.last_epoch states = torch.load(hydra.retrieved_checkpoint.linked_files()[0]) model.load_state_dict(states['model']) optimizer.load_state_dict(states['optimizer']) scheduler.load_state_dict(states['scheduler']) epoch_train_losses, epoch_val_losses = [], [] epoch_train_accs, epoch_val_accs = [], [] train_loader = DataLoader(trainset, batch_size=params['batch_size'], shuffle=True, collate_fn=dataset.collate) val_loader = DataLoader(valset, batch_size=params['batch_size'], shuffle=False, collate_fn=dataset.collate) test_loader = DataLoader(testset, batch_size=params['batch_size'], shuffle=False, collate_fn=dataset.collate) last_hydra_checkpoint = start0 # At any point you can hit Ctrl + C to break out of training early. try: with tqdm(range(start_epoch, params['epochs']), mininterval=params['hydra_progress_bar_every'], maxinterval=None, unit='epoch', initial=start_epoch, total=params['epochs']) as t: for epoch in t: t.set_description('Epoch %d' % epoch) start = time.time() if MODEL_NAME in ['RingGNN', '3WLGNN']: # since different batch training function for dense GNNs epoch_train_loss, epoch_train_acc, optimizer = train_epoch(model, optimizer, device, train_loader, epoch, params['batch_size']) else: # for all other models common train function epoch_train_loss, epoch_train_acc, optimizer = train_epoch(model, optimizer, device, train_loader, epoch) epoch_val_loss, epoch_val_acc = evaluate_network(model, device, val_loader, epoch) _, epoch_test_acc = evaluate_network(model, device, test_loader, epoch) epoch_train_losses.append(epoch_train_loss) epoch_val_losses.append(epoch_val_loss) epoch_train_accs.append(epoch_train_acc) epoch_val_accs.append(epoch_val_acc) writer.add_scalar('train/_loss', epoch_train_loss, epoch) writer.add_scalar('val/_loss', epoch_val_loss, epoch) writer.add_scalar('train/_acc', epoch_train_acc, epoch) writer.add_scalar('val/_acc', epoch_val_acc, epoch) writer.add_scalar('test/_acc', epoch_test_acc, epoch) writer.add_scalar('learning_rate', optimizer.param_groups[0]['lr'], epoch) t.set_postfix(time=time.time() - start, lr=optimizer.param_groups[0]['lr'], train_loss=epoch_train_loss, val_loss=epoch_val_loss, train_acc=epoch_train_acc, val_acc=epoch_val_acc, test_acc=epoch_test_acc) print('') per_epoch_time.append(time.time() - start) # Saving checkpoint ckpt_dir = os.path.join(root_ckpt_dir, "RUN_") if not os.path.exists(ckpt_dir): os.makedirs(ckpt_dir) torch.save(model.state_dict(), '{}.pkl'.format(ckpt_dir + "/epoch_" + str(epoch))) files = glob.glob(ckpt_dir + '/*.pkl') for file in files: epoch_nb = file.split('_')[-1] epoch_nb = int(epoch_nb.split('.')[0]) if epoch_nb < epoch - 1: os.remove(file) scheduler.step(epoch_val_loss) if optimizer.param_groups[0]['lr'] < params['min_lr']: print("\n!! LR SMALLER OR EQUAL TO MIN LR THRESHOLD.") break # Stop training after params['max_time'] hours if time.time() - start0 > params['max_time'] * 3600: print('-' * 89) print("Max_time for training elapsed {:.2f} hours, so stopping".format(params['max_time'])) break # Saving checkpoint if hydra.is_available() and (time.time() - last_hydra_checkpoint) > params[ 'hydra_checkpoint_every']: last_hydra_checkpoint = time.time() ck_path = '/tmp/epoch_{}.pkl'.format(epoch + 1) torch.save({ 'model': model.state_dict(), 'optimizer': optimizer.state_dict(), 'scheduler': scheduler.state_dict() }, ck_path) ck = hydra.checkpoint() ck.last_epoch = epoch + 1 ck.time_elapsed = time.time() - start0 # save best epoch ck.link_file(ck_path) ck.save_to_server() if hydra.is_available() and epoch % params['hydra_eta_every'] == 0: hydra.set_eta(per_epoch_time[-1] * (params['epochs'] - epoch - 1)) except KeyboardInterrupt: print('-' * 89) print('Exiting from training early because of KeyboardInterrupt') _, test_acc = evaluate_network(model, device, test_loader, epoch) _, val_acc = evaluate_network(model, device, val_loader, epoch) _, train_acc = evaluate_network(model, device, train_loader, epoch) print("Test Accuracy: {:.4f}".format(test_acc)) print("Train Accuracy: {:.4f}".format(train_acc)) print("Convergence Time (Epochs): {:.4f}".format(epoch)) print("TOTAL TIME TAKEN: {:.4f}s".format(time.time() - start0)) print("AVG TIME PER EPOCH: {:.4f}s".format(np.mean(per_epoch_time))) writer.close() if hydra.is_available(): hydra.save_output({'loss': {'train': epoch_train_losses, 'val': epoch_val_losses}, 'acc': {'train': epoch_train_acc, 'val': epoch_val_acc}}, 'history') hydra.save_output( {'test_acc': test_acc, 'train_acc': train_acc, 'val_acc': val_acc, 'total_time': time.time() - start0, 'avg_epoch_time': np.mean(per_epoch_time)}, 'summary') """ Write the results in out_dir/results folder """ with open(write_file_name + '.txt', 'w') as f: f.write("""Dataset: {},\nModel: {}\n\nparams={}\n\nnet_params={}\n\n{}\n\nTotal Parameters: {}\n\n FINAL RESULTS\nTEST ACCURACY: {:.4f}\nTRAIN ACCURACY: {:.4f}\n\n Convergence Time (Epochs): {:.4f}\nTotal Time Taken: {:.4f} hrs\nAverage Time Per Epoch: {:.4f} s\n\n\n""" \ .format(DATASET_NAME, MODEL_NAME, params, net_params, model, net_params['total_param'], test_acc, train_acc, epoch, (time.time() - start0) / 3600, np.mean(per_epoch_time)))
def train_val_pipeline(MODEL_NAME, dataset, params, net_params, dirs): start0 = time.time() per_epoch_time = [] DATASET_NAME = dataset.name if MODEL_NAME in ['GCN', 'GAT']: if net_params['self_loop']: print("[!] Adding graph self-loops for GCN/GAT models (central node trick).") dataset._add_self_loops() trainset, valset, testset = dataset.train, dataset.val, dataset.test root_log_dir, root_ckpt_dir, write_file_name, write_config_file = dirs device = net_params['device'] # Write network and optimization hyper-parameters in folder config/ with open(write_config_file + '.txt', 'w') as f: f.write("""Dataset: {},\nModel: {}\n\nparams={}\n\nnet_params={}\n\n\nTotal Parameters: {}\n\n""" .format(DATASET_NAME, MODEL_NAME, params, net_params, net_params['total_param'])) log_dir = os.path.join(root_log_dir, "RUN_" + str(0)) writer = SummaryWriter(log_dir=log_dir) print("Log Dir: %s" % log_dir) # setting seeds random.seed(params['seed']) np.random.seed(params['seed']) torch.manual_seed(params['seed']) if device == 'cuda': torch.cuda.manual_seed(params['seed']) print("Training Graphs: ", len(trainset)) print("Validation Graphs: ", len(valset)) print("Test Graphs: ", len(testset)) print("Number of Classes: ", net_params['n_classes']) print("Number of Layers: %s" % net_params['L']) print("Enable My Layer: %s" % net_params['my_layer']) model = gnn_model(MODEL_NAME, net_params) model = model.to(device) optimizer = optim.Adam(model.parameters(), lr=params['init_lr'], weight_decay=params['weight_decay']) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=params['lr_reduce_factor'], patience=params['lr_schedule_patience'], verbose=True) epoch_train_losses, epoch_val_losses, epoch_test_losses = [], [], [] epoch_train_accs, epoch_val_accs, epoch_test_accs = [], [], [] train_loader = DataLoader(trainset, batch_size=params['batch_size'], shuffle=True, collate_fn=dataset.collate) val_loader = DataLoader(valset, batch_size=params['batch_size'], shuffle=False, collate_fn=dataset.collate) test_loader = DataLoader(testset, batch_size=params['batch_size'], shuffle=False, collate_fn=dataset.collate) start_time_str = time.strftime('%Hh%Mm%Ss on %b %d %Y') # At any point you can hit Ctrl + C to break out of training early. try: with tqdm(range(params['epochs'])) as t: for epoch in t: t.set_description('Epoch %d' % epoch) start = time.time() epoch_train_loss, epoch_train_acc, optimizer = train_epoch(model, optimizer, device, train_loader, epoch) epoch_val_loss, epoch_val_acc = evaluate_network(model, device, val_loader, epoch) epoch_test_loss, epoch_test_acc = evaluate_network(model, device, test_loader, epoch) epoch_train_losses.append(epoch_train_loss) epoch_val_losses.append(epoch_val_loss) epoch_test_losses.append(epoch_test_loss) epoch_train_accs.append(epoch_train_acc) epoch_val_accs.append(epoch_val_acc) epoch_test_accs.append(epoch_test_acc) writer.add_scalar('train/_loss', epoch_train_loss, epoch) writer.add_scalar('val/_loss', epoch_val_loss, epoch) writer.add_scalar('test/_loss', epoch_test_loss, epoch) writer.add_scalar('train/_acc', epoch_train_acc, epoch) writer.add_scalar('val/_acc', epoch_val_acc, epoch) writer.add_scalar('test/_acc', epoch_test_acc, epoch) writer.add_scalar('learning_rate', optimizer.param_groups[0]['lr'], epoch) t.set_postfix(time=time.time()-start, lr=optimizer.param_groups[0]['lr'], train_loss=epoch_train_loss, val_loss=epoch_val_loss, train_acc=epoch_train_acc, val_acc=epoch_val_acc, test_acc=epoch_test_acc) per_epoch_time.append(time.time()-start) # Saving checkpoint ckpt_dir = os.path.join(root_ckpt_dir, "RUN_") if not os.path.exists(ckpt_dir): os.makedirs(ckpt_dir) torch.save(model.state_dict(), '{}.pkl'.format(ckpt_dir + "/epoch_" + str(epoch))) files = glob.glob(ckpt_dir + '/*.pkl') for file in files: epoch_nb = file.split('_')[-1] epoch_nb = int(epoch_nb.split('.')[0]) if epoch_nb < epoch-1: os.remove(file) scheduler.step(epoch_val_loss) if optimizer.param_groups[0]['lr'] < params['min_lr']: print("\n!! LR SMALLER OR EQUAL TO MIN LR THRESHOLD.") break # Stop training after params['max_time'] hours if time.time()-start0 > params['max_time']*3600: print('-' * 89) print("Max_time for training elapsed {:.2f} hours, so stopping".format(params['max_time'])) break except KeyboardInterrupt: print('-' * 89) print('Exiting from training early because of KeyboardInterrupt') _, test_acc = evaluate_network(model, device, test_loader, epoch) _, train_acc = evaluate_network(model, device, train_loader, epoch) print("Test Accuracy: {:.4f}".format(test_acc)) print("Train Accuracy: {:.4f}".format(train_acc)) print("TOTAL TIME TAKEN: {:.4f}s".format(time.time()-start0)) print("AVG TIME PER EPOCH: {:.4f}s".format(np.mean(per_epoch_time))) writer.close() end_time_str = time.strftime('%Hh%Mm%Ss on %b %d %Y') """ Write the results in out_dir/results folder """ result_txt = """ Log Dir: {}\nStart Time: {}\nEnd Time: {}\n\n Dataset: {},\nModel: {}\n\nparams={}\n\nnet_params={}\n\n{}\n\nTotal Parameters: {}\n\n FINAL RESULTS\nTEST ACCURACY: {:.4f}\nTRAIN ACCURACY: {:.4f}\n\n Total Time Taken: {:.4f} hrs\nAverage Time Per Epoch: {:.4f} s\n\n\n"""\ .format(log_dir, start_time_str, end_time_str, DATASET_NAME, MODEL_NAME, params, net_params, model, net_params['total_param'], test_acc, train_acc, (time.time()-start0)/3600, np.mean(per_epoch_time)) with open(write_file_name + '.txt', 'w') as f: print("Writing results to %s" % write_file_name + '.txt') f.write(result_txt) # send results to gmail try: from gmail import send subject = 'Result for Dataset: {}, Model: {}'.format(DATASET_NAME, MODEL_NAME) body = """Dataset: {},\nModel: {}\n\nparams={}\n\nnet_params={}\n\n{}\n\nTotal Parameters: {}\n\n FINAL RESULTS\nTEST ACCURACY: {:.4f}\nTRAIN ACCURACY: {:.4f}\n\n Total Time Taken: {:.4f} hrs\nAverage Time Per Epoch: {:.4f} s\n\n\n"""\ .format(DATASET_NAME, MODEL_NAME, params, net_params, model, net_params['total_param'], test_acc, train_acc, (time.time()-start0)/3600, np.mean(per_epoch_time)) send(subject, body) except: pass