def train_val_pipeline(MODEL_NAME, DATASET_NAME, params, net_params, dirs):
avg_test_acc = []
avg_train_acc = []
avg_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()
if net_params['pos_enc']:
print("[!] Adding graph positional encoding.")
dataset._add_positional_encodings(net_params['pos_enc_dim']) #TODO
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(5):
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
# drop_last = False
if MODEL_NAME in ['RingGNN', '3WLGNN']:
# import train functions specific for WL-GNNs
from train.train_CSL_graph_classification import train_epoch_dense as train_epoch, evaluate_network_dense as evaluate_network
from functools import partial # util function to pass pos_enc flag to collate function
train_loader = DataLoader(trainset,
shuffle=True,
collate_fn=partial(
dataset.collate_dense_gnn,
pos_enc=net_params['pos_enc']))
val_loader = DataLoader(valset,
shuffle=False,
collate_fn=partial(
dataset.collate_dense_gnn,
pos_enc=net_params['pos_enc']))
test_loader = DataLoader(testset,
shuffle=False,
collate_fn=partial(
dataset.collate_dense_gnn,
pos_enc=net_params['pos_enc']))
else:
# import train functions for all other GCNs
from train.train_CSL_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_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_train_acc = 100. * epoch_train_acc
epoch_test_acc = 100. * epoch_test_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)
# 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_epochs.append(epoch)
print("Test Accuracy [LAST EPOCH]: {:.4f}".format(test_acc))
print("Train Accuracy [LAST EPOCH]: {:.4f}".format(train_acc))
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)))
# Final test accuracy value averaged over 5-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: {:.3f}\n with test acc s.d. {:.3f}\nTRAIN ACCURACY averaged: {:.3f}\n with train s.d. {:.3f}\n\n
Convergence Time (Epochs): {:.3f}\nTotal Time Taken: {:.3f} hrs\nAverage Time Per Epoch: {:.3f} s\n\n\nAll Splits Test Accuracies: {}\n\nAll Splits Train 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(np.array(avg_epochs)),
(time.time() - t0) / 3600, np.mean(per_epoch_time), avg_test_acc, avg_train_acc))
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