def inference(MODEL_NAME, dataset, params, net_params, model_path):
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
device = net_params['device']
model = gnn_model(MODEL_NAME, net_params)
model = model.to(device)
model.load_state_dict(torch.load(model_path))
# batching exception for Diffpool
drop_last = True if MODEL_NAME == 'DiffPool' else False
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)
_, train_acc = evaluate_network(model, device, train_loader)
_, val_acc = evaluate_network(model, device, val_loader)
_, test_acc = evaluate_network(model, device, test_loader)
print("Train ACC: {:.4f}".format(train_acc))
print("Val ACC: {:.4f}".format(val_acc))
print("Test ACC: {:.4f}".format(test_acc))
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()
if MODEL_NAME in ['SoGCN']:
if model_ls[0]['net_params']['undirected']:
print("[!] Converting directed graphs to undirected graphs for SoGCN model.")
dataset._to_undirected()
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']
# 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):
t0 = 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 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']))
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 == '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 = [], []
# batching exception for Diffpool
drop_last = True if MODEL_NAME == 'DiffPool' else False
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)
# 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_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('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_")
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 > 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() - t0))
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
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'],
np.mean(np.array(test_acc))*100, np.mean(np.array(train_acc))*100, (time.time()-t0)/3600, np.mean(per_epoch_time)))
# 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'],
np.mean(np.array(test_acc))*100, np.mean(np.array(train_acc))*100, (time.time()-t0)/3600, np.mean(per_epoch_time))
send(subject, body)
except:
pass
def train_val_pipeline(dataset, params, net_params):
t0 = 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 == 'cuda':
torch.cuda.manual_seed(params['seed'])
print("Training Graphs: ", len(trainset))
print("Validation Graphs: ", len(valset))
print("Test Graphs: ", len(testset))
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,
net_params['augmentation'], net_params['flip'],
net_params['distortion'])
epoch_val_loss, epoch_val_acc = evaluate_network(
model, device, val_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)
_, 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)
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 > 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("Val Accuracy: {:.4f}".format(val_acc))
print("Train Accuracy: {:.4f}".format(train_acc))
print("TOTAL TIME TAKEN: {:.4f}s".format(time.time() - t0))
print("AVG TIME PER EPOCH: {:.4f}s".format(np.mean(per_epoch_time)))
def train_val_pipeline(MODEL_NAME, dataset, params, net_params, args):
load_model = args.load_model
aug_type_list = [
'drop_nodes', 'drop_add_edges', 'noise', 'mask', 'subgraph', 'new',
'random', 'random2'
]
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
device = net_params['device']
# 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('-' * 40 + "Finetune Option" + '-' * 40)
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'
# output_path = './001_mask_models_03'
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)
# batching exception for Diffpool
drop_last = True if MODEL_NAME == 'DiffPool' else False
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)
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_acc = evaluate_network(model, device, test_loader, epoch)
print('-' * 80)
print("Epoch [{}] 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 EQUAL TO MIN LR SET.")
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))
def train_val_pipeline(MODEL_NAME, dataset, params, net_params, dirs):
t0 = time.time()
per_epoch_time = []
DATASET_NAME = dataset.name
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']))
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 == '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))
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:
print('not the first exec.')
print(t0, 'old')
t0 -= hydra.retrieved_checkpoint.time_elapsed
print(t0, 'new')
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 = t0
# 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()
epoch_train_loss, epoch_train_acc, optimizer = train_epoch(
model, optimizer, device, train_loader, epoch,
net_params['augmentation'], net_params['flip'])
epoch_val_loss, epoch_val_acc = evaluate_network(
model, device, val_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('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)
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 > 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() - t0
# 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("Val Accuracy: {:.4f}".format(val_acc))
print("Train Accuracy: {:.4f}".format(train_acc))
print("TOTAL TIME TAKEN: {:.4f}s".format(time.time() - t0))
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_accs,
'val': epoch_val_accs
}
}, 'history')
hydra.save_output(
{
'test_acc': test_acc,
'train_acc': train_acc,
'val_acc': val_acc,
'total_time': time.time() - t0,
'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
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'],
np.mean(np.array(test_acc)) * 100, np.mean(np.array(train_acc)) * 100,
(time.time() - t0) / 3600, np.mean(per_epoch_time)))