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, params, net_params, dirs):
t0 = time.time()
per_epoch_time = []
DATASET_NAME = dataset.name
#assert net_params['self_loop'] == False, "No self-loop support for %s dataset" % 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.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_f1s, epoch_val_f1s = [], []
if MODEL_NAME in ['RingGNN', '3WLGNN']:
# import train functions specific for WL-GNNs
from train.train_TSP_edge_classification import train_epoch_dense as train_epoch, evaluate_network_dense as evaluate_network
from functools import partial # util function to pass edge_feat to collate function
train_loader = DataLoader(trainset,
shuffle=True,
collate_fn=partial(
dataset.collate_dense_gnn,
edge_feat=net_params['edge_feat']))
val_loader = DataLoader(valset,
shuffle=False,
collate_fn=partial(
dataset.collate_dense_gnn,
edge_feat=net_params['edge_feat']))
test_loader = DataLoader(testset,
shuffle=False,
collate_fn=partial(
dataset.collate_dense_gnn,
edge_feat=net_params['edge_feat']))
else:
# import train functions for all other GCNs
from train.train_TSP_edge_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,
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()
if MODEL_NAME in [
'RingGNN', '3WLGNN'
]: # since different batch training function for dense GNNs
epoch_train_loss, epoch_train_f1, 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_f1, optimizer = train_epoch(
model, optimizer, device, train_loader, epoch)
epoch_val_loss, epoch_val_f1 = evaluate_network(
model, device, val_loader, epoch)
_, epoch_test_f1 = evaluate_network(model, device, test_loader,
epoch)
epoch_train_losses.append(epoch_train_loss)
epoch_val_losses.append(epoch_val_loss)
epoch_train_f1s.append(epoch_train_f1)
epoch_val_f1s.append(epoch_val_f1)
writer.add_scalar('train/_loss', epoch_train_loss, epoch)
writer.add_scalar('val/_loss', epoch_val_loss, epoch)
writer.add_scalar('train/_f1', epoch_train_f1, epoch)
writer.add_scalar('val/_f1', epoch_val_f1, epoch)
writer.add_scalar('test/_f1', epoch_test_f1, 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_f1=epoch_train_f1,
val_f1=epoch_val_f1,
test_f1=epoch_test_f1)
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_f1 = evaluate_network(model, device, test_loader, epoch)
_, train_f1 = evaluate_network(model, device, train_loader, epoch)
print("Test F1: {:.4f}".format(test_f1))
print("Train F1: {:.4f}".format(train_f1))
print("Convergence Time (Epochs): {:.4f}".format(epoch))
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 F1: {:.4f}\nTRAIN F1: {:.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'],
np.mean(np.array(test_f1)), np.mean(np.array(train_f1)), epoch, (time.time()-t0)/3600, np.mean(per_epoch_time)))