def main():
prog_args = configs.arg_parse()
path = os.path.join(prog_args.logdir, io_utils.gen_prefix(prog_args))
writer = SummaryWriter(path)
if prog_args.gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = prog_args.cuda
print("CUDA", prog_args.cuda)
else:
print("Using CPU")
# use --bmname=[dataset_name] for Reddit-Binary, Mutagenicity
if prog_args.bmname is not None:
benchmark_task(prog_args, writer=writer)
elif prog_args.pkl_fname is not None:
pkl_task(prog_args)
elif prog_args.dataset is not None:
if prog_args.dataset == "syn1":
syn_task1(prog_args, writer=writer)
elif prog_args.dataset == "syn2":
syn_task2(prog_args, writer=writer)
elif prog_args.dataset == "syn3":
syn_task3(prog_args, writer=writer)
elif prog_args.dataset == "syn4":
syn_task4(prog_args, writer=writer)
elif prog_args.dataset == "syn5":
syn_task5(prog_args, writer=writer)
elif prog_args.dataset == "enron":
enron_task(prog_args, writer=writer)
elif prog_args.dataset == "ppi_essential":
ppi_essential_task(prog_args, writer=writer)
writer.close()
def main():
prog_args = configs.arg_parse()
path = os.path.join(prog_args.logdir, io_utils.gen_prefix(prog_args))
writer = SummaryWriter(path)
if prog_args.gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = prog_args.cuda
print("CUDA", prog_args.cuda)
else:
print("Using CPU")
syn_task1(prog_args, 30, writer=writer)
syn_task2(prog_args, writer=writer)
syn_task1(prog_args, 300, writer=writer)
syn_task1(prog_args, 3000, writer=writer)
writer.close()
def main():
prog_args = configs.arg_parse()
path = os.path.join(prog_args.logdir, io_utils.gen_prefix(prog_args))
writer = SummaryWriter(path)
if prog_args.gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = prog_args.cuda
print("CUDA", prog_args.cuda)
else:
print("Using CPU")
# use --bmname=[dataset_name] for Reddit-Binary, Mutagenicity
if prog_args.dataset == "FFMpeg":
FFMpeg(prog_args, writer=writer)
elif prog_args.dataset == "fan":
fan(prog_args)
elif prog_args.dataset == "reveal":
reveal(prog_args, writer=writer)
writer.close()
def main():
# Parsing defaults for all program parameters unless provided by user
prog_args = parse_train_args.arg_parse()
syn_task1(prog_args, writer=None) # writer=writer
path = os.path.join(prog_args.logdir, io_utils.gen_prefix(prog_args))
print("Tensorboard writer path :\n", path)
# writer = SummaryWriter(path)
if prog_args.gpu:
# os.environ["CUDA_VISIBLE_DEVICES"] = prog_args.cuda
# env = os.environ.get('CUDA_VISIBLE_DEVICES')
# print("Environment is set :", env)
print('\nCUDA_VISIBLE_DEVICES')
print('------------------------------------------')
print("CUDA", prog_args.cuda)
else:
print('\n------------------------------------------')
print("Using CPU")
print('Batch size : {} and Dropout rate : {}'.format(
prog_args.batch_size, prog_args.dropout))
print('------------------------------------------')
for epoch in range(args.num_epochs):
total_loss = 0
model.train()
for batch in loader:
# print ('batch:', batch.feat)
opt.zero_grad()
pred = model(batch)
pred = pred[train_mask]
# print ('pred:', pred)
label = labels[train_mask]
# print ('label:', label)
loss = model.loss(pred, label)
print('loss:', loss)
loss.backward()
opt.step()
total_loss += loss.item() * 1
total_loss /= num_train
writer.add_scalar("loss", total_loss, epoch)
if epoch % 10 == 0:
test_acc = test(loader, model, args, labels, test_mask)
print("Epoch {}. Loss: {:.4f}. Test accuracy: {:.4f}".format(
epoch, total_loss, test_acc))
writer.add_scalar("test accuracy", test_acc, epoch)
prog_args = arg_parse()
path = os.path.join(prog_args.logdir, io_utils.gen_prefix(prog_args))
syn_task1(prog_args, writer=SummaryWriter(path))
def main():
# Parsing defaults for all program parameters unless provided by user
prog_args = parse_explainer_args.arg_parse()
# More params on top of train.py
prog_args.writer = None # Check is for None and default is True
path = os.path.join(prog_args.logdir, io_utils.gen_prefix(prog_args))
print("Tensorboard writer path :\n", path)
print("No. of epochs :", prog_args.num_epochs)
# writer = SummaryWriter(path)
if prog_args.gpu:
# os.environ["CUDA_VISIBLE_DEVICES"] = prog_args.cuda
# env = os.environ.get('CUDA_VISIBLE_DEVICES')
# print("Environment is set :", env)
print('\nCUDA_VISIBLE_DEVICES')
print('------------------------------------------')
print("CUDA", prog_args.cuda)
else:
print('\n------------------------------------------')
print("Using CPU")
# Loading previously saved computational graph data (model checkpoint)
model_dict = io_utils.load_ckpt(prog_args)
model_optimizer = model_dict['optimizer']
print("Model optimizer :", model_optimizer)
print("Model optimizer state dictionary :\n",
model_optimizer.state_dict()['param_groups'])
# model.load_state_dict(checkpoint['model_state_dict'])
# optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
# epoch = checkpoint['epoch']
# loss = checkpoint['loss']
print(
'------------------------------------------------------------------------------------'
)
print("Keys in loaded model dictionary :", list(model_dict))
print("Keys in loaded model optimizer dictionary:",
list(model_optimizer.state_dict()))
print("All loaded labels :\n", model_dict['cg']['label'])
print()
print('mask_act:{}, mask_bias:{}, explainer_suffix:{}'.format(
prog_args.mask_act, prog_args.mask_bias, prog_args.explainer_suffix))
# Determine explainer mode
graph_mode = (prog_args.graph_mode or prog_args.multigraph_class >= 0
or prog_args.graph_idx >= 0)
# Trained data stored in computational graph dictionary
cg_dict = model_dict['cg']
input_dim = cg_dict['feat'].shape[2]
num_classes = cg_dict['pred'].shape[2]
print("\nLoaded model from subdirectory \"{}\" ...".format(
prog_args.ckptdir))
print("input dim :", input_dim, "; num classes :", num_classes)
print("Labels of retrieved data :\n", cg_dict['label'])
print(
'------------------------------------------------------------------------------------'
)
print("Multigraph class :", prog_args.multigraph_class)
print("Graph Index :", prog_args.graph_idx)
print("Explainer graph mode :", graph_mode)
print("Input dimension :", input_dim)
print("Hidden dimension :", prog_args.hidden_dim)
print("Output dimension :", prog_args.output_dim)
print("Number of classes :", num_classes)
print("Number of GCN layers :", prog_args.num_gc_layers)
print("Batch Normalization :", prog_args.bn)
model = models.GcnEncoderNode(input_dim=input_dim,
hidden_dim=prog_args.hidden_dim,
embedding_dim=prog_args.output_dim,
label_dim=num_classes,
num_layers=prog_args.num_gc_layers,
bn=prog_args.bn,
args=prog_args)
print("\nGcnEncoderNode model :\n", model)
# load state_dict (obtained by model.state_dict() when saving checkpoint)
# Loading Model for Inference
print("Model checked result :",
model.load_state_dict(model_dict['model_state']))
print(
'------------------------------------------------------------------------------------\n'
)
# Explaining single node prediction
print('Explaining single default node :', prog_args.explain_node)
# The number of epochs used for explanation training is much smaller than the 1K epochs used for node label
# trainings and predictions in the GCN. The former is trained only based on the k-hop labels which depends
# on the number GCN layers (at a smaller scale, so the number of epochs can be lower without reducing the
# accuracy). Whereas, the latter will affect the node predictions and thus, it will affect the accuracy of
# the node explanations.
print('GNN Explainer is trained based on {} epochs.'.format(
prog_args.num_epochs))
print("Writer :", prog_args.writer)
# Create explainer
explainer = explain.Explainer(
model=model,
adj=cg_dict["adj"],
feat=cg_dict["feat"],
label=cg_dict["label"],
pred=cg_dict["pred"],
train_idx=cg_dict["train_idx"],
args=prog_args,
writer=prog_args.writer,
print_training=True,
graph_mode=graph_mode,
graph_idx=prog_args.graph_idx,
)
if prog_args.explain_node is not None:
# Returned masked adjacency, edges and features of the subgraph
masked_adj, masked_edges, masked_features = explainer.explain(
prog_args.explain_node, unconstrained=False)
print("Returned masked adjacency matrix :\n", masked_adj)
print("Returned masked edges matrix :\n", masked_edges)
print("Returned masked features matrix :\n", masked_features)
else:
print("Please provide node for explanation.")
