def main_get_mask(args, imp_num, rewind_weight_mask=None, rewind_predict_weight=None, resume_train_ckpt=None):
device = torch.device("cuda:" + str(args.device))
dataset = PygLinkPropPredDataset(name=args.dataset)
data = dataset[0]
# Data(edge_index=[2, 2358104], edge_weight=[2358104, 1], edge_year=[2358104, 1], x=[235868, 128])
split_edge = dataset.get_edge_split()
evaluator = Evaluator(args.dataset)
x = data.x.to(device)
edge_index = data.edge_index.to(device)
args.in_channels = data.x.size(-1)
args.num_tasks = 1
model = DeeperGCN(args).to(device)
pruning.add_mask(model, args)
predictor = LinkPredictor(args).to(device)
pruning.add_trainable_mask_noise(model, args, c=1e-4)
optimizer = torch.optim.Adam(list(model.parameters()) + list(predictor.parameters()), lr=args.lr)
results = {'epoch': 0 }
keys = ['highest_valid', 'final_train', 'final_test', 'highest_train']
hits = ['Hits@10', 'Hits@50', 'Hits@100']
for key in keys:
results[key] = {k: 0 for k in hits}
start_epoch = 1
if resume_train_ckpt:
start_epoch = resume_train_ckpt['epoch']
rewind_weight_mask = resume_train_ckpt['rewind_weight_mask']
ori_model_dict = model.state_dict()
over_lap = {k : v for k, v in resume_train_ckpt['model_state_dict'].items() if k in ori_model_dict.keys()}
ori_model_dict.update(over_lap)
model.load_state_dict(ori_model_dict)
print("Resume at IMP:[{}] epoch:[{}] len:[{}/{}]!".format(imp_num, resume_train_ckpt['epoch'], len(over_lap.keys()), len(ori_model_dict.keys())))
optimizer.load_state_dict(resume_train_ckpt['optimizer_state_dict'])
adj_spar, wei_spar = pruning.print_sparsity(model, args)
else:
rewind_weight_mask = copy.deepcopy(model.state_dict())
rewind_predict_weight = copy.deepcopy(predictor.state_dict())
for epoch in range(start_epoch, args.mask_epochs + 1):
t0 = time.time()
epoch_loss, prune_info_dict = train.train_mask(model, predictor,
x,
edge_index,
split_edge,
optimizer, args)
result = train.test(model, predictor,
x,
edge_index,
split_edge,
evaluator,
args.batch_size, args)
k = 'Hits@50'
train_result, valid_result, test_result = result[k]
if train_result > results['highest_train'][k]:
results['highest_train'][k] = train_result
if valid_result > results['highest_valid'][k]:
results['highest_valid'][k] = valid_result
results['final_train'][k] = train_result
results['final_test'][k] = test_result
results['epoch'] = epoch
pruning.save_all(model, predictor,
rewind_weight_mask,
optimizer,
imp_num,
epoch,
args.model_save_path,
'IMP{}_train_ckpt'.format(imp_num))
epoch_time = (time.time() - t0) / 60
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ' | ' +
'IMP:[{}] (GET Mask) Epoch:[{}/{}] LOSS:[{:.4f}] Train :[{:.2f}] Valid:[{:.2f}] Test:[{:.2f}] | Update Test:[{:.2f}] at epoch:[{}] | Adj[{:.3f}%] Wei[{:.3f}%] Time:[{:.2f}min]'
.format(imp_num, epoch, args.mask_epochs,
epoch_loss,
train_result * 100,
valid_result * 100,
test_result * 100,
results['final_test'][k] * 100,
results['epoch'],
prune_info_dict['adj_spar'],
prune_info_dict['wei_spar'],
epoch_time))
rewind_weight_mask, adj_spar, wei_spar = pruning.change(rewind_weight_mask, model, args)
print('-' * 100)
print("INFO : IMP:[{}] (GET MASK) Final Result Train:[{:.2f}] Valid:[{:.2f}] Test:[{:.2f}] | Adj:[{:.3f}%] Wei:[{:.3f}%]"
.format(imp_num, results['final_train'][k] * 100,
results['highest_valid'][k] * 100,
results['final_test'][k] * 100,
adj_spar,
wei_spar))
print('-' * 100)
return rewind_weight_mask, rewind_predict_weight
def main_fixed_mask(args, imp_num, rewind_weight_mask, rewind_predict_weight, resume_train_ckpt=None):
device = torch.device("cuda:" + str(args.device))
dataset = PygLinkPropPredDataset(name=args.dataset)
data = dataset[0]
# Data(edge_index=[2, 2358104], edge_weight=[2358104, 1], edge_year=[2358104, 1], x=[235868, 128])
split_edge = dataset.get_edge_split()
evaluator = Evaluator(args.dataset)
x = data.x.to(device)
edge_index = data.edge_index.to(device)
args.in_channels = data.x.size(-1)
args.num_tasks = 1
model = DeeperGCN(args).to(device)
pruning.add_mask(model, args)
predictor = LinkPredictor(args).to(device)
model.load_state_dict(rewind_weight_mask)
predictor.load_state_dict(rewind_predict_weight)
adj_spar, wei_spar = pruning.print_sparsity(model, args)
for name, param in model.named_parameters():
if 'mask' in name:
param.requires_grad = False
optimizer = torch.optim.Adam(list(model.parameters()) + list(predictor.parameters()), lr=args.lr)
# results = {}
results = {'epoch': 0 }
keys = ['highest_valid', 'final_train', 'final_test', 'highest_train']
hits = ['Hits@10', 'Hits@50', 'Hits@100']
for key in keys:
results[key] = {k: 0 for k in hits}
results['adj_spar'] = adj_spar
results['wei_spar'] = wei_spar
start_epoch = 1
if resume_train_ckpt is not None:
start_epoch = resume_train_ckpt['epoch']
ori_model_dict = model.state_dict()
over_lap = {k : v for k, v in resume_train_ckpt['model_state_dict'].items() if k in ori_model_dict.keys()}
ori_model_dict.update(over_lap)
model.load_state_dict(ori_model_dict)
predictor.load_state_dict(resume_train_ckpt['predictor_state_dict'])
print("(FIXED MASK) Resume at epoch:[{}] len:[{}/{}]!".format(resume_train_ckpt['epoch'], len(over_lap.keys()), len(ori_model_dict.keys())))
optimizer.load_state_dict(resume_train_ckpt['optimizer_state_dict'])
adj_spar, wei_spar = pruning.print_sparsity(model, args)
for epoch in range(start_epoch, args.fix_epochs + 1):
t0 = time.time()
epoch_loss = train.train_fixed(model, predictor, x, edge_index, split_edge, optimizer, args.batch_size, args)
result = train.test(model, predictor, x, edge_index, split_edge, evaluator, args.batch_size, args)
# return a tuple
k = 'Hits@50'
train_result, valid_result, test_result = result[k]
if train_result > results['highest_train'][k]:
results['highest_train'][k] = train_result
if valid_result > results['highest_valid'][k]:
results['highest_valid'][k] = valid_result
results['final_train'][k] = train_result
results['final_test'][k] = test_result
results['epoch'] = epoch
pruning.save_all(model, predictor,
rewind_weight_mask,
optimizer,
imp_num,
epoch,
args.model_save_path,
'IMP{}_fixed_ckpt'.format(imp_num))
epoch_time = (time.time() - t0) / 60
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ' | ' +
'IMP:[{}] (FIX Mask) Epoch:[{}/{}] LOSS:[{:.4f}] Train :[{:.2f}] Valid:[{:.2f}] Test:[{:.2f}] | Update Test:[{:.2f}] at epoch:[{}] Time:[{:.2f}min]'
.format(imp_num, epoch, args.fix_epochs, epoch_loss, train_result * 100,
valid_result * 100,
test_result * 100,
results['final_test'][k] * 100,
results['epoch'],
epoch_time))
print("=" * 120)
print("INFO final: IMP:[{}], Train:[{:.2f}] Best Val:[{:.2f}] at epoch:[{}] | Final Test Acc:[{:.2f}] Adj:[{:.2f}%] Wei:[{:.2f}%]"
.format(imp_num, results['final_train'][k] * 100,
results['highest_valid'][k] * 100,
results['epoch'],
results['final_test'][k] * 100,
results['adj_spar'],
results['wei_spar']))
print("=" * 120)
plot_pointcloud(new_block2, title="iter: %d" % i)
plot_pointcloud(new_block3, title="iter: %d" % i)
plt.imshow(image.squeeze().permute(1, 2, 0).detach().cpu().numpy())
plt.show()
plt.plot(range(len(losses)), np.log10(losses))
plt.show()
save_obj("model1.obj", new_block1.verts_packed(), new_block1.faces_packed())
save_obj("model2.obj", new_block2.verts_packed(), new_block2.faces_packed())
save_obj("model3.obj", new_block3.verts_packed(), new_block3.faces_packed())
if i % save_period == 0 and i !=0:
print("saving ...")
if WANDB:
torch.save({
'epoch': epoch,
'iteration':i,
'gcn_state_dict': [gcn1.state_dict(), gcn2.state_dict(), gcn3.state_dict()],
'fe_state_dic': fe.state_dict(),
'cnn_state_dic': image_model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': loss}, os.path.join(wandb.run.dir, 'dic_checkpoint'))
# Optimization step
loss.backward()
#torch.nn.utils.clip_grad_norm_(gcn1.parameters(), 1)
#torch.nn.utils.clip_grad_norm_(gcn2.parameters(), 1)
#torch.nn.utils.clip_grad_norm_(gcn3.parameters(), 1)
#torch.nn.utils.clip_grad_norm_(image_model.parameters(), 1)
#plot_grad_flow(image_model.named_parameters())
#plt.show()
def main_fixed_mask(args, imp_num, final_state_dict=None, resume_train_ckpt=None):
device = torch.device("cuda:" + str(args.device))
dataset = OGBNDataset(dataset_name=args.dataset)
nf_path = dataset.extract_node_features(args.aggr)
args.num_tasks = dataset.num_tasks
args.nf_path = nf_path
evaluator = Evaluator(args.dataset)
criterion = torch.nn.BCEWithLogitsLoss()
valid_data_list = []
for i in range(args.num_evals):
parts = dataset.random_partition_graph(dataset.total_no_of_nodes, cluster_number=args.valid_cluster_number)
valid_data = dataset.generate_sub_graphs(parts, cluster_number=args.valid_cluster_number)
valid_data_list.append(valid_data)
print("-" * 120)
model = DeeperGCN(args).to(device)
pruning.add_mask(model)
if final_state_dict is not None:
pruning.retrain_operation(dataset, model, final_state_dict)
adj_spar, wei_spar = pruning.print_sparsity(dataset, model)
for name, param in model.named_parameters():
if 'mask' in name:
param.requires_grad = False
optimizer = optim.Adam(model.parameters(), lr=args.lr)
results = {'highest_valid': 0, 'final_train': 0, 'final_test': 0, 'highest_train': 0, 'epoch':0}
results['adj_spar'] = adj_spar
results['wei_spar'] = wei_spar
start_epoch = 1
if resume_train_ckpt:
dataset.adj = resume_train_ckpt['adj']
start_epoch = resume_train_ckpt['epoch']
rewind_weight_mask = resume_train_ckpt['rewind_weight_mask']
ori_model_dict = model.state_dict()
over_lap = {k : v for k, v in resume_train_ckpt['model_state_dict'].items() if k in ori_model_dict.keys()}
ori_model_dict.update(over_lap)
model.load_state_dict(ori_model_dict)
print("Resume at IMP:[{}] epoch:[{}] len:[{}/{}]!".format(imp_num, resume_train_ckpt['epoch'], len(over_lap.keys()), len(ori_model_dict.keys())))
optimizer.load_state_dict(resume_train_ckpt['optimizer_state_dict'])
adj_spar, wei_spar = pruning.print_sparsity(dataset, model)
for epoch in range(start_epoch, args.epochs + 1):
# do random partition every epoch
t0 = time.time()
train_parts = dataset.random_partition_graph(dataset.total_no_of_nodes, cluster_number=args.cluster_number)
data = dataset.generate_sub_graphs(train_parts, cluster_number=args.cluster_number, ifmask=True)
epoch_loss = train.train_fixed(data, dataset, model, optimizer, criterion, device, args)
result = train.multi_evaluate(valid_data_list, dataset, model, evaluator, device)
train_result = result['train']['rocauc']
valid_result = result['valid']['rocauc']
test_result = result['test']['rocauc']
if valid_result > results['highest_valid']:
results['highest_valid'] = valid_result
results['final_train'] = train_result
results['final_test'] = test_result
results['epoch'] = epoch
final_state_dict = pruning.save_all(dataset,
model,
None,
optimizer,
imp_num,
epoch,
args.model_save_path,
'IMP{}_fixed_ckpt'.format(imp_num))
epoch_time = (time.time() - t0) / 60
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ' | ' +
'IMP:[{}] (FIX Mask) Epoch[{}/{}] LOSS[{:.4f}] Train[{:.2f}] Valid[{:.2f}] Test[{:.2f}] | Update Test[{:.2f}] at epoch[{}] | Adj[{:.2f}%] Wei[{:.2f}%] Time[{:.2f}min]'
.format(imp_num, epoch, args.epochs, epoch_loss, train_result * 100,
valid_result * 100,
test_result * 100,
results['final_test'] * 100,
results['epoch'],
results['adj_spar'] * 100,
results['wei_spar'] * 100,
epoch_time))
print("=" * 120)
print("INFO final: IMP:[{}], Train:[{:.2f}] Best Val:[{:.2f}] at epoch:[{}] | Final Test Acc:[{:.2f}] | Adj:[{:.2f}%] Wei:[{:.2f}%]"
.format(imp_num, results['final_train'] * 100,
results['highest_valid'] * 100,
results['epoch'],
results['final_test'] * 100,
results['adj_spar'] * 100,
results['wei_spar'] * 100))
print("=" * 120)
def main_fixed_mask(args,
imp_num,
adj_percent,
wei_percent,
resume_train_ckpt=None):
device = torch.device("cuda:" + str(args.device))
dataset = PygNodePropPredDataset(name=args.dataset)
data = dataset[0]
split_idx = dataset.get_idx_split()
evaluator = Evaluator(args.dataset)
x = data.x.to(device)
y_true = data.y.to(device)
train_idx = split_idx['train'].to(device)
edge_index = data.edge_index.to(device)
edge_index = to_undirected(edge_index, data.num_nodes)
if args.self_loop:
edge_index = add_self_loops(edge_index, num_nodes=data.num_nodes)[0]
args.in_channels = data.x.size(-1)
args.num_tasks = dataset.num_classes
model = DeeperGCN(args).to(device)
pruning.add_mask(model, args.num_layers)
pruning.random_pruning(model, args, adj_percent, wei_percent)
adj_spar, wei_spar = pruning.print_sparsity(model, args)
for name, param in model.named_parameters():
if 'mask' in name:
param.requires_grad = False
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
results = {
'highest_valid': 0,
'final_train': 0,
'final_test': 0,
'highest_train': 0,
'epoch': 0
}
results['adj_spar'] = adj_spar
results['wei_spar'] = wei_spar
start_epoch = 1
if resume_train_ckpt:
start_epoch = resume_train_ckpt['epoch']
ori_model_dict = model.state_dict()
over_lap = {
k: v
for k, v in resume_train_ckpt['model_state_dict'].items()
if k in ori_model_dict.keys()
}
ori_model_dict.update(over_lap)
model.load_state_dict(ori_model_dict)
print("(RP FIXED MASK) Resume at epoch:[{}] len:[{}/{}]!".format(
resume_train_ckpt['epoch'], len(over_lap.keys()),
len(ori_model_dict.keys())))
optimizer.load_state_dict(resume_train_ckpt['optimizer_state_dict'])
adj_spar, wei_spar = pruning.print_sparsity(model, args)
for epoch in range(start_epoch, args.epochs + 1):
epoch_loss = train_fixed(model, x, edge_index, y_true, train_idx,
optimizer, args)
result = test(model, x, edge_index, y_true, split_idx, evaluator)
train_accuracy, valid_accuracy, test_accuracy = result
if valid_accuracy > results['highest_valid']:
results['highest_valid'] = valid_accuracy
results['final_train'] = train_accuracy
results['final_test'] = test_accuracy
results['epoch'] = epoch
pruning.save_all(model, None, optimizer, imp_num, epoch,
args.model_save_path,
'RP{}_fixed_ckpt'.format(imp_num))
print(
time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ' | ' +
'RP:[{}] (FIX Mask) Epoch:[{}/{}]\t LOSS:[{:.4f}] Train :[{:.2f}] Valid:[{:.2f}] Test:[{:.2f}] | Update Test:[{:.2f}] at epoch:[{}]'
.format(imp_num, epoch, args.epochs, epoch_loss, train_accuracy *
100, valid_accuracy * 100, test_accuracy *
100, results['final_test'] * 100, results['epoch']))
print("=" * 120)
print(
"syd final: RP:[{}], Train:[{:.2f}] Best Val:[{:.2f}] at epoch:[{}] | Final Test Acc:[{:.2f}] Adj:[{:.2f}%] Wei:[{:.2f}%]"
.format(imp_num, results['final_train'] * 100,
results['highest_valid'] * 100, results['epoch'],
results['final_test'] * 100, results['adj_spar'],
results['wei_spar']))
print("=" * 120)
def main():
EPOCHS = 1
NUMBER_OF_SUBGRAPHS = 10
CKPT_PATH = f'neighbor_deeper_num_{NUMBER_OF_SUBGRAPHS}.pt'
EXPERIMENT_RES_PATH = f'neighbor_deeper_num_{NUMBER_OF_SUBGRAPHS}_experiment_res.pk'
args = ArgsInit().args
if args.use_gpu:
device = torch.device("cuda:" +
str(args.device)) if torch.cuda.is_available(
) else torch.device("cpu")
else:
device = torch.device('cpu')
dataset = PygNodePropPredDataset(name=args.dataset)
graph = dataset[0]
print(graph)
num_parts = NUMBER_OF_SUBGRAPHS
data_list = list(
NeighborSubgraphLoader(graph, num_parts=NUMBER_OF_SUBGRAPHS))
print(f'len of datalist: {len(data_list)}')
number_of_train = int(0.9 * num_parts)
train_data_list = data_list[0:number_of_train]
test_data_list = data_list[number_of_train:]
print(
f'Train test split successful, number of train: {len(train_data_list)} | number of test: {len(test_data_list)}'
)
args.in_channels = graph.x.size(-1)
args.num_tasks = dataset.num_classes
logging.info('%s' % args)
model = DeeperGCN(args).to(device)
logging.info(model)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
start_time = time.time()
epoch_loss_list = []
epoch_acc_list = []
highest_acc = 0
best_model_dict = None
for epoch in range(1, EPOCHS + 1):
epoch_loss, epoch_acc = train(data_list, model, optimizer, device)
epoch_loss_list.append(epoch_loss)
epoch_acc_list.append(epoch_acc)
print('Epoch {}, training loss {:.4f} | training acc {}'.format(
epoch, epoch_loss, epoch_acc))
test_acc = test(model, test_data_list)
if test_acc > highest_acc:
highest_acc = test_acc
best_model_dict = model.state_dict()
logging.info(
f'best test acc: {highest_acc} | saved to path {CKPT_PATH}')
end_time = time.time()
total_time = end_time - start_time
logging.info('Total time: {}'.format(
time.strftime('%H:%M:%S', time.gmtime(total_time))))
experiment_result = {}
experiment_result['Total training time'] = total_time
experiment_result['Epoch loss list'] = epoch_loss_list
experiment_result['Epoch acc list'] = epoch_acc_list
experiment_result['Best test acc'] = highest_acc
torch.save(best_model_dict, CKPT_PATH)
with open(EXPERIMENT_RES_PATH, 'wb') as f:
pk.dump(experiment_result, f)
def main_get_mask(args,
imp_num,
rewind_weight_mask=None,
resume_train_ckpt=None):
device = torch.device("cuda:" + str(args.device))
dataset = PygNodePropPredDataset(name=args.dataset)
data = dataset[0]
split_idx = dataset.get_idx_split()
evaluator = Evaluator(args.dataset)
x = data.x.to(device)
y_true = data.y.to(device)
train_idx = split_idx['train'].to(device)
edge_index = data.edge_index.to(device)
edge_index = to_undirected(edge_index, data.num_nodes)
if args.self_loop:
edge_index = add_self_loops(edge_index, num_nodes=data.num_nodes)[0]
args.in_channels = data.x.size(-1)
args.num_tasks = dataset.num_classes
print("-" * 120)
model = DeeperGCN(args).to(device)
pruning.add_mask(model, args.num_layers)
if rewind_weight_mask:
model.load_state_dict(rewind_weight_mask)
adj_spar, wei_spar = pruning.print_sparsity(model, args)
pruning.add_trainable_mask_noise(model, args, c=1e-5)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
results = {
'highest_valid': 0,
'final_train': 0,
'final_test': 0,
'highest_train': 0,
'epoch': 0
}
start_epoch = 1
if resume_train_ckpt:
start_epoch = resume_train_ckpt['epoch']
rewind_weight_mask = resume_train_ckpt['rewind_weight_mask']
ori_model_dict = model.state_dict()
over_lap = {
k: v
for k, v in resume_train_ckpt['model_state_dict'].items()
if k in ori_model_dict.keys()
}
ori_model_dict.update(over_lap)
model.load_state_dict(ori_model_dict)
print("Resume at IMP:[{}] epoch:[{}] len:[{}/{}]!".format(
imp_num, resume_train_ckpt['epoch'], len(over_lap.keys()),
len(ori_model_dict.keys())))
optimizer.load_state_dict(resume_train_ckpt['optimizer_state_dict'])
adj_spar, wei_spar = pruning.print_sparsity(model, args)
else:
rewind_weight_mask = copy.deepcopy(model.state_dict())
for epoch in range(start_epoch, args.mask_epochs + 1):
epoch_loss = train(model, x, edge_index, y_true, train_idx, optimizer,
args)
result = test(model, x, edge_index, y_true, split_idx, evaluator)
train_accuracy, valid_accuracy, test_accuracy = result
if valid_accuracy > results['highest_valid']:
results['highest_valid'] = valid_accuracy
results['final_train'] = train_accuracy
results['final_test'] = test_accuracy
results['epoch'] = epoch
rewind_weight_mask, adj_spar, wei_spar = pruning.get_final_mask_epoch(
model, rewind_weight_mask, args)
#pruning.save_all(model, rewind_weight_mask, optimizer, imp_num, epoch, args.model_save_path, 'IMP{}_train_ckpt'.format(imp_num))
print(
time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ' | ' +
'IMP:[{}] (GET Mask) Epoch:[{}/{}]\t LOSS:[{:.4f}] Train :[{:.2f}] Valid:[{:.2f}] Test:[{:.2f}] | Update Test:[{:.2f}] at epoch:[{}] | Adj:[{:.2f}%] Wei:[{:.2f}%]'
.format(imp_num, epoch, args.mask_epochs, epoch_loss,
train_accuracy * 100, valid_accuracy * 100, test_accuracy *
100, results['final_test'] *
100, results['epoch'], adj_spar, wei_spar))
print('-' * 100)
print(
"INFO : IMP:[{}] (GET MASK) Final Result Train:[{:.2f}] Valid:[{:.2f}] Test:[{:.2f}] | Adj:[{:.2f}%] Wei:[{:.2f}%] "
.format(imp_num, results['final_train'] * 100,
results['highest_valid'] * 100, results['final_test'] * 100,
adj_spar, wei_spar))
print('-' * 100)
return rewind_weight_mask