def view_model_param(MODEL_NAME, net_params):
model = gnn_model(MODEL_NAME, net_params)
total_param = 0
print("MODEL DETAILS:\n")
#print(model)
for param in model.parameters():
# print(param.data.size())
total_param += np.prod(list(param.data.size()))
print('MODEL/Total parameters:', MODEL_NAME, total_param)
return total_param
def train_val_pipeline(MODEL_NAME, DATASET_NAME, params, net_params, dirs):
avg_test_acc = []
avg_train_acc = []
avg_convergence_epochs = []
t0 = time.time()
per_epoch_time = []
dataset = LoadData(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 the 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']))
# At any point you can hit Ctrl + C to break out of training early.
try:
for split_number in range(10):
t0_split = time.time()
log_dir = os.path.join(root_log_dir, "RUN_" + str(split_number))
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("RUN NUMBER: ", split_number)
trainset, valset, testset = dataset.train[
split_number], dataset.val[split_number], dataset.test[
split_number]
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 = [], []
# batching exception for Diffpool
drop_last = True if MODEL_NAME == 'DiffPool' else False
if MODEL_NAME in ['RingGNN', '3WLGNN']:
# import train functions specific for WL-GNNs
from train.train_TUs_graph_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_TUs_graph_classification import train_epoch_sparse as train_epoch, evaluate_network_sparse as evaluate_network
train_loader = DataLoader(trainset,
batch_size=params['batch_size'],
shuffle=True,
drop_last=drop_last,
collate_fn=dataset.collate)
val_loader = DataLoader(valset,
batch_size=params['batch_size'],
shuffle=False,
drop_last=drop_last,
collate_fn=dataset.collate)
test_loader = DataLoader(testset,
batch_size=params['batch_size'],
shuffle=False,
drop_last=drop_last,
collate_fn=dataset.collate)
with tqdm(range(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)
_, epoch_test_acc = evaluate_network(
model, device, test_loader, 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_" + str(split_number))
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 EQUAL TO MIN LR SET.")
break
# Stop training after params['max_time'] hours
if time.time() - t0_split > params[
'max_time'] * 3600 / 10: # Dividing max_time by 10, since there are 10 runs in TUs
print('-' * 89)
print(
"Max_time for one train-val-test split experiment elapsed {:.3f} hours, so stopping"
.format(params['max_time'] / 10))
break
_, test_acc = evaluate_network(model, device, test_loader, epoch)
_, train_acc = evaluate_network(model, device, train_loader, epoch)
avg_test_acc.append(test_acc)
avg_train_acc.append(train_acc)
avg_convergence_epochs.append(epoch)
print("Test Accuracy [LAST EPOCH]: {:.4f}".format(test_acc))
print("Train Accuracy [LAST EPOCH]: {:.4f}".format(train_acc))
print("Convergence Time (Epochs): {:.4f}".format(epoch))
except KeyboardInterrupt:
print('-' * 89)
print('Exiting from training early because of KeyboardInterrupt')
print("TOTAL TIME TAKEN: {:.4f}hrs".format((time.time() - t0) / 3600))
print("AVG TIME PER EPOCH: {:.4f}s".format(np.mean(per_epoch_time)))
print("AVG CONVERGENCE Time (Epochs): {:.4f}".format(
np.mean(np.array(avg_convergence_epochs))))
# Final test accuracy value averaged over 10-fold
print(
"""\n\n\nFINAL RESULTS\n\nTEST ACCURACY averaged: {:.4f} with s.d. {:.4f}"""
.format(
np.mean(np.array(avg_test_acc)) * 100,
np.std(avg_test_acc) * 100))
print("\nAll splits Test Accuracies:\n", avg_test_acc)
print(
"""\n\n\nFINAL RESULTS\n\nTRAIN ACCURACY averaged: {:.4f} with s.d. {:.4f}"""
.format(
np.mean(np.array(avg_train_acc)) * 100,
np.std(avg_train_acc) * 100))
print("\nAll splits Train Accuracies:\n", avg_train_acc)
writer.close()
"""
Write the results in out/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 averaged: {:.4f} with s.d. {:.4f}\nTRAIN ACCURACY averaged: {:.4f} with s.d. {:.4f}\n\n
Average Convergence Time (Epochs): {:.4f} with s.d. {:.4f}\nTotal Time Taken: {:.4f} hrs\nAverage Time Per Epoch: {:.4f} s\n\n\nAll Splits Test Accuracies: {}"""\
.format(DATASET_NAME, MODEL_NAME, params, net_params, model, net_params['total_param'],
np.mean(np.array(avg_test_acc))*100, np.std(avg_test_acc)*100,
np.mean(np.array(avg_train_acc))*100, np.std(avg_train_acc)*100,
np.mean(avg_convergence_epochs), np.std(avg_convergence_epochs),
(time.time()-t0)/3600, np.mean(per_epoch_time), avg_test_acc))
val_losses, val_accuracy = [], []
test_losses, test_accuracy = [], []
for idx_split, (d_train, d_val, d_test) in enumerate(split):
split.set_description(f'Split #{idx_split}')
set_seed(params['seed'])
train_loader = DataLoader(d_train, batch_size=args.batch_size, shuffle=True, collate_fn=dataset.collate)
val_loader = DataLoader(d_val, batch_size=args.batch_size, collate_fn=dataset.collate)
test_loader = DataLoader(d_test, batch_size=args.batch_size, collate_fn=dataset.collate)
net_param = deepcopy(_net_param)
net_param['in_dim'] = in_dim
net_param['n_classes'] = dataset.all.num_labels
net = gnn_model(args.net, net_param)
net.cuda()
optimizer = optim.Adam(net.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'])
with tqdm(range(params['epochs'])) as epochs:
for e in epochs:
epochs.set_description(f'Epoch #{e}')
train_loss, train_acc = train(net, train_loader, optimizer)
val_loss, val_acc = val(net, val_loader)
epochs.set_postfix(lr=optimizer.param_groups[0]['lr'],