Hi guys, I recently made a GNN model using TransformerConv and TopKPooling, it is smooth while training, but I have problems when I want to use it to predict, it kept telling me that the TransformerConv doesn’t have the ‘aggr_module’ attribute
This is my network:
class GNN(torch.nn.Module):
def __init__(self, feature_size, model_params):
super(GNN, self).__init__()
embedding_size = model_params["model_embedding_size"]
n_heads = model_params["model_attention_heads"]
self.n_layers = model_params["model_layers"]
dropout_rate = model_params["model_dropout_rate"]
top_k_ratio = model_params["model_top_k_ratio"]
self.top_k_every_n = model_params["model_top_k_every_n"]
dense_neurons = model_params["model_dense_neurons"]
self.conv_layers = ModuleList([])
self.transf_layers = ModuleList([])
self.pooling_layers = ModuleList([])
self.bn_layers = ModuleList([])
# Transformation layer: transform original node features to embedding vector(size: embedding_size(defined in config.py))
self.conv1 = TransformerConv(feature_size,
embedding_size,
heads=n_heads,
dropout=dropout_rate,
#edge_dim=edge_dim,
beta=True)
self.transf1 = Linear(embedding_size*n_heads, embedding_size)
self.bn1 = BatchNorm1d(embedding_size)
# Other layers: message passing and pooling
for i in range(self.n_layers):
self.conv_layers.append(TransformerConv(embedding_size,
embedding_size,
heads=n_heads,
dropout=dropout_rate,
#edge_dim=edge_dim,
beta=True))
# map conv_layer output size back to emgedding_size(embedding_size*n_heads -> embedding_size)
self.transf_layers.append(Linear(embedding_size*n_heads, embedding_size))
# Batch normalization
self.bn_layers.append(BatchNorm1d(embedding_size))
# Top-k pooling to reduce the size of the graph
if i % self.top_k_every_n == 0:
self.pooling_layers.append(TopKPooling(embedding_size, ratio=top_k_ratio))
# Linear output layers: feed graph representation in & reduce until single value left
self.linear1 = Linear(embedding_size*2, dense_neurons)
self.linear2 = Linear(dense_neurons, int(dense_neurons/2))
self.linear3 = Linear(int(dense_neurons/2), 3) # same as the general form
def forward(self, x, edge_index, batch_index):
# Initial transformation
x = self.conv1(x, edge_index)
x = torch.relu(self.transf1(x))
x = torch.relu((x))
x = self.bn1(x)
# Holds the intermediate graph representations
global_representation = []
for i in range(self.n_layers):
x = self.conv_layers[i](x, edge_index)
x = torch.relu(self.transf_layers[i](x))
x = torch.relu((x))
x = self.bn_layers[i](x)
# Always aggregate last layer
if i % self.top_k_every_n == 0 or i == self.n_layers:
x , edge_index, edge_attr, batch_index, _, _ = self.pooling_layers[int(i/self.top_k_every_n)]( x,
edge_index,
None,
batch_index)
# Add current representation
global_representation.append(torch.cat([gmp(x, batch_index), gap(x, batch_index)], dim=1))
x = sum(global_representation)
# Output block
x = torch.relu(self.linear1(x))
x = F.dropout(x, p=0.8, training=self.training)
x = torch.relu(self.linear2(x))
x = F.dropout(x, p=0.8, training=self.training)
x = self.linear3(x)
return x
One training:
def run_one_training(params):
params = params[0]
with mlflow.start_run() as run:
# Log parameters used in this experiment
for key in params.keys():
mlflow.log_param(key, params[key])
# Loading the dataset
print("Loading dataset...")
full_dataset = ProcessedDataset(root = "data/", filename = "fixtures_full.csv")
full_dataset.shuffle()
train_dataset = full_dataset[:3400] # around 80% of the full dataset
test_dataset = full_dataset[3400:3800]
# Prepare training
print("Preparing Training")
batch_size=params["batch_size"]
train_loader = DataLoader(train_dataset, batch_size=batch_size)
test_loader = DataLoader(test_dataset, batch_size=batch_size)
# Loading the model
print("Loading model...")
model_params = {k: v for k, v in params.items() if k.startswith("model_")}
model = GNN(feature_size=train_dataset[0].x.shape[1], model_params=model_params)
print(model)
model = model.to(device)
print(f"Number of parameters: {count_parameters(model)}")
mlflow.log_param("num_params", count_parameters(model))
class_weights = [1.0239, 1.2753, 0.8070]
class_weights= torch.tensor(class_weights,dtype=torch.float)
loss_fn = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(),
lr=params["learning_rate"],
momentum=params["sgd_momentum"],
weight_decay=params["weight_decay"])
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=params["scheduler_gamma"])
# Start training
best_loss = 1000
early_stopping_counter = 0
for epoch in range(300):
if early_stopping_counter <= 10: # = x * 5
# Training
model.train()
loss = train_one_epoch(epoch, model, train_loader, optimizer, loss_fn)
print(f"Epoch {epoch} | Train Loss {loss}")
mlflow.log_metric(key="Train loss", value=float(loss), step=epoch)
# Testing
model.eval()
if epoch % 5 == 0:
loss = test(epoch, model, test_loader, loss_fn)
print(f"Epoch {epoch} | Test Loss {loss}")
mlflow.log_metric(key="Test loss", value=float(loss), step=epoch)
# Update best loss
if float(loss) < best_loss:
best_loss = loss
# Save the currently best model
mlflow.pytorch.log_model(model, "model", signature=SIGNATURE)
early_stopping_counter = 0
else:
early_stopping_counter += 1
scheduler.step()
else:
print("Early stopping due to no improvement.")
return [best_loss]
print(f"Finishing training with best test loss: {best_loss}")
return [best_loss]
Train and Test
def train_one_epoch(epoch, model, train_loader, optimizer, loss_fn):
# Enumerate over the data
all_preds = []
all_labels = []
running_loss = 0.0
step = 0
for _, batch in enumerate(tqdm(train_loader)):
batch.x = torch.tensor(batch.x)
batch.x = batch.x.reshape((-1, *batch.x.shape[2:]))
# Use GPU
batch.to(device)
# Reset gradients
optimizer.zero_grad()
# Passing the node features and the connection info
pred = model(torch.tensor(batch.x).float(),
#batch.edge_attr.float(),
batch.edge_index,
batch.batch)
# Calculating the loss and gradients
loss = torch.sqrt(loss_fn(pred, batch.y.long()))
loss.backward()
optimizer.step()
# Update tracking
running_loss += loss.item()
step += 1
all_preds.append(np.argmax(pred.cpu().detach().numpy(), axis=1))
all_labels.append(batch.y.cpu().detach().numpy())
all_preds = np.concatenate(all_preds).ravel()
all_labels = np.concatenate(all_labels).ravel()
calculate_metrics(all_preds, all_labels, epoch, "train")
return running_loss/step
def test(epoch, model, test_loader, loss_fn):
all_preds = []
all_preds_raw = []
all_labels = []
running_loss = 0.0
step = 0
for batch in test_loader:
batch.x = torch.tensor(batch.x)
batch.x = batch.x.reshape((-1, *batch.x.shape[2:]))
batch.to(device)
pred = model(torch.tensor(batch.x).float(),
#batch.edge_attr.float(),
batch.edge_index,
batch.batch)
loss = torch.sqrt(loss_fn(pred, batch.y.long()))
# Update tracking
running_loss += loss.item()
step += 1
all_preds.append(np.argmax(pred.cpu().detach().numpy(), axis=1))
all_preds_raw.append(torch.sigmoid(pred).cpu().detach().numpy())
all_labels.append(batch.y.cpu().detach().numpy())
all_preds = np.concatenate(all_preds).ravel()
all_labels = np.concatenate(all_labels).ravel()
print(all_preds_raw[0][:10])
print(all_preds[:10])
print(all_labels[:10])
calculate_metrics(all_preds, all_labels, epoch, "test")
log_conf_matrix(all_preds, all_labels, epoch)
return running_loss/step
Predict:
def predict(model, test_loader):
all_preds = []
all_preds_raw = []
all_labels = []
for batch in test_loader:
batch.x = torch.tensor(batch.x)
batch.x = batch.x.reshape((-1, *batch.x.shape[2:]))
batch.to(device)
pred = model(torch.tensor(batch.x).float(),
#batch.edge_attr.float(),
batch.edge_index,
batch.batch)
all_preds.append(np.argmax(pred.cpu().detach().numpy(), axis=1))
all_preds_raw.append(torch.sigmoid(pred).cpu().detach().numpy())
all_labels.append(batch.y.cpu().detach().numpy())
all_preds = np.concatenate(all_preds).ravel()
all_labels = np.concatenate(all_labels).ravel()
return all_preds, all_preds_raw, all_labels
I was using mlflow to load my model and this is what I did:
import mlflow
logged_model = 'runs:/b18929aa871047f9892aa3c84a998d28/model'
# Load model
loaded_model = mlflow.pytorch.load_model(logged_model)
loaded_model = loaded_model.to(device)
loaded_model.eval()
loader = DataLoader(dataset, batch_size=len(dataset))
all_pred, all_pred_raw, all_label = predict(loaded_model, loader)
This is the error message
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Input In [23], in <cell line: 7>()
3 dataset = full_dataset[3800:]
5 loader = DataLoader(dataset, batch_size=len(dataset))
----> 7 all_pred, all_pred_raw, all_label = predict(loaded_model, loader)
Input In [20], in predict(epoch, model, test_loader, loss_fn)
143 batch.x = batch.x.reshape((-1, *batch.x.shape[2:]))
144 batch.to(device)
--> 145 pred = model(torch.tensor(batch.x).float(),
146 #batch.edge_attr.float(),
147 batch.edge_index,
148 batch.batch)
150 all_preds.append(np.argmax(pred.cpu().detach().numpy(), axis=1))
151 all_preds_raw.append(torch.sigmoid(pred).cpu().detach().numpy())
File ~\anaconda3\lib\site-packages\torch\nn\modules\module.py:1190, in Module._call_impl(self, *input, **kwargs)
1186 # If we don't have any hooks, we want to skip the rest of the logic in
1187 # this function, and just call forward.
1188 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1189 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190 return forward_call(*input, **kwargs)
1191 # Do not call functions when jit is used
1192 full_backward_hooks, non_full_backward_hooks = [], []
File ~\FIFA_PROJECT\model.py:67, in GNN.forward(self, x, edge_index, batch_index)
63 def forward(self, x, edge_index, batch_index):
64 #def forward(self, x, edge_attr=None, edge_index, batch_index):
65 # Initial transformation
66 #x = self.conv1(x, edge_index, edge_attr)
---> 67 x = self.conv1(x, edge_index)
68 x = torch.relu(self.transf1(x))
69 x = torch.relu((x))
File ~\anaconda3\lib\site-packages\torch\nn\modules\module.py:1190, in Module._call_impl(self, *input, **kwargs)
1186 # If we don't have any hooks, we want to skip the rest of the logic in
1187 # this function, and just call forward.
1188 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1189 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190 return forward_call(*input, **kwargs)
1191 # Do not call functions when jit is used
1192 full_backward_hooks, non_full_backward_hooks = [], []
File ~\anaconda3\lib\site-packages\torch_geometric\nn\conv\gcn_conv.py:198, in GCNConv.forward(self, x, edge_index, edge_weight)
195 x = self.lin(x)
197 # propagate_type: (x: Tensor, edge_weight: OptTensor)
--> 198 out = self.propagate(edge_index, x=x, edge_weight=edge_weight,
199 size=None)
201 if self.bias is not None:
202 out = out + self.bias
File ~\anaconda3\lib\site-packages\torch_geometric\nn\conv\message_passing.py:454, in MessagePassing.propagate(self, edge_index, size, **kwargs)
451 if res is not None:
452 aggr_kwargs = res[0] if isinstance(res, tuple) else res
--> 454 out = self.aggregate(out, **aggr_kwargs)
456 for hook in self._aggregate_forward_hooks.values():
457 res = hook(self, (aggr_kwargs, ), out)
File ~\anaconda3\lib\site-packages\torch_geometric\nn\conv\message_passing.py:578, in MessagePassing.aggregate(self, inputs, index, ptr, dim_size)
565 def aggregate(self, inputs: Tensor, index: Tensor,
566 ptr: Optional[Tensor] = None,
567 dim_size: Optional[int] = None) -> Tensor:
568 r"""Aggregates messages from neighbors as
569 :math:`\square_{j \in \mathcal{N}(i)}`.
570
(...)
576 as specified in :meth:`__init__` by the :obj:`aggr` argument.
577 """
--> 578 return self.aggr_module(inputs, index, ptr=ptr, dim_size=dim_size,
579 dim=self.node_dim)
File ~\anaconda3\lib\site-packages\torch\nn\modules\module.py:1265, in Module.__getattr__(self, name)
1263 if name in modules:
1264 return modules[name]
-> 1265 raise AttributeError("'{}' object has no attribute '{}'".format(
1266 type(self).__name__, name))
AttributeError: 'TransformerConv' object has no attribute 'aggr_module'
Please I’m begging 