I am new in Pytorch. I got this error when I was trying to train the GCN model on my custom dataset.
The error:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
/tmp/ipykernel_61/1753868253.py in <module>
30 # Run for 200 epochs (range is exclusive in the upper bound)
31 for epoch in range(1, 201):
---> 32 train()
33 train_acc = test(train_loader)
34 test_acc = test(test_loader)
/tmp/ipykernel_61/1753868253.py in train()
10 for data in train_loader:
11 print('x_shape:',data.x.shape)
---> 12 out = model(data.x.float(), data.edge_index, data.batch)
13 loss = criterion(out, data.num_classes)
14 loss.backward()
RuntimeError: [enforce fail at CPUAllocator.cpp:68] . DefaultCPUAllocator: can't allocate memory: you tried to allocate 8609587200 bytes. Error code 12 (Cannot allocate memory)
My code:
train_loader = DataLoader(trainset, batch_size=64, shuffle=True)
test_loader = DataLoader(testset, batch_size=64, shuffle=False)
# Define our GCN class as a pytorch Module
class GCN(torch.nn.Module):
def __init__(self, hidden_channels):
super(GCN, self).__init__()
# We inherit from pytorch geometric's GCN class, and we initialize three layers
self.conv1 = GCNConv(data.num_features, hidden_channels)
self.conv2 = GCNConv(hidden_channels, hidden_channels)
self.conv3 = GCNConv(hidden_channels, hidden_channels)
# Our final linear layer will define our output
self.lin = Linear(hidden_channels, 8)
def forward(self, x, edge_index, batch):
# 1. Obtain node embeddings
x = self.conv1(x, edge_index)
x = x.relu()
x = self.conv2(x, edge_index)
x = x.relu()
x = self.conv3(x, edge_index)
# 2. Readout layer
x = global_mean_pool(x, batch) # [batch_size, hidden_channels]
# 3. Apply a final classifier
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin(x)
return x
model = GCN(hidden_channels=64)