def forward(self, data):
subgraph_data = subgraph_loader(data, k, super_node_size, num_tours,
num_cpus)
subgraphs = [
get_subgraph(data[subgraph_data.batch[i].item()],
subgraph_data.subgraphs[i].squeeze())
for i in range(len(subgraph_data.subgraphs))
]
subgraphs_lst = []
for i in range(0, len(subgraphs), 500):
subgraphs_b = Batch().from_data_list(
subgraphs[i:i + min([500, len(subgraphs) - i])])
subgraphs_b = self.gnn_layer(subgraphs_b.x.cuda(), subgraphs_b.edge_index.cuda(), subgraphs_b.batch.cuda()) \
if next(self.parameters()).get_device() != -1 else self.gnn_layer(subgraphs_b.x, subgraphs_b.edge_index, subgraphs_b.batch)
subgraphs_lst.append(subgraphs_b)
subgraphs = torch.cat(subgraphs_lst, dim=0)
subgraphs = self.output_layer(subgraphs)
weights = subgraph_data.weights.cuda() if next(
self.parameters()).get_device() != -1 else subgraph_data.weights
batch = subgraph_data.batch.cuda() if next(
self.parameters()).get_device() != -1 else subgraph_data.batch
subgraphs = subgraphs * weights
norm = global_add_pool(weights, batch)
energy = global_add_pool(subgraphs, batch)
return energy / norm
""" Creating samplers to parallelize over each minibatch """
for i in range(1,batch_size+1):
process = subprocess.Popen("cp external_libs/rgpm/tradicional external_libs/rgpm/tradicional-"+str(i), stdout=subprocess.PIPE, stderr=None, shell=True, stdin=subprocess.PIPE)
process.wait()
model = Net()
model.apply(init_weights)
best_model = copy.deepcopy(model)
best_loss = float("inf")
cn_wait = 0
step = 0
print("===\t RANDOM MODEL RESULTS \t===")
print("hyperedge:\t", evaluate.logit(train_x=best_model.embedding([ get_subgraph(graph, train["Hyperedge"]["x"][i].squeeze()) for i in range(len(train["Hyperedge"]["x"])) ]).detach().cpu(), train_y=train["Hyperedge"]["y"], \
test_x=best_model.embedding([ get_subgraph(test_graph, test["Hyperedge"]["x"][i].squeeze()) for i in range(len(test["Hyperedge"]["x"])) ]).detach().cpu(), test_y=test["Hyperedge"]["y"] ) )
print("===\t \t===")
if cuda: model = model.cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
loss_fn = torch.nn.BCEWithLogitsLoss()
random.shuffle(dataset)
stime = time.time()
for i in range(0, len(dataset), batch_size):
model.train()
optimizer.zero_grad()
y = torch.tensor([graph.y for graph in dataset[i:i+min([batch_size,len(dataset)-i])]]).float().unsqueeze(-1)
if cuda: y = y.cuda()
energy = model(dataset[i:i+min([batch_size,len(dataset)-i])])
stdout=subprocess.PIPE,
stderr=None,
shell=True,
stdin=subprocess.PIPE)
process.wait()
model = Net()
model.apply(init_weights)
best_model = copy.deepcopy(model)
best_loss = float("inf")
cn_wait = 0
step = 0
print("===\t RANDOM MODEL RESULTS \t===")
print("dag:\t", evaluate.logit(train_x=best_model.embedding([ get_subgraph(graph, train["DAG"]["x"][i].squeeze()) for i in range(len(train["DAG"]["x"])) ]).detach().cpu(), train_y=train["DAG"]["y"], \
test_x=best_model.embedding([ get_subgraph(graph, test["DAG"]["x"][i].squeeze()) for i in range(len(test["DAG"]["x"])) ]).detach().cpu(), test_y=test["DAG"]["y"] ), \
"hyperedge:\t", evaluate.logit(train_x=best_model.embedding([ get_subgraph(graph, train["Hyperedge"]["x"][i].squeeze()) for i in range(len(train["Hyperedge"]["x"])) ]).detach().cpu(), train_y=train["Hyperedge"]["y"], \
test_x=best_model.embedding([ get_subgraph(graph, test["Hyperedge"]["x"][i].squeeze()) for i in range(len(test["Hyperedge"]["x"])) ]).detach().cpu(), test_y=test["Hyperedge"]["y"] ) )
print("===\t \t===")
if cuda: model = model.cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
loss_fn = torch.nn.BCEWithLogitsLoss()
train_subsample_ids = list(range(len(train["DAG"]["x"])))
test_subsample_ids = list(range(len(test["DAG"]["x"])))
random.shuffle(train_subsample_ids)
random.shuffle(test_subsample_ids)
train_subsample_ids = train_subsample_ids[:5000]
test_subsample_ids = test_subsample_ids[:5000]
