There is a very [large graph] in my code and I am trying to break it [into sub-graph]
A sad story is that when I try this over my graph:
my_graph = dgl.graph(edge_list, idtype=dtype)
item_set = gb.ItemSet(torch.arange(my_graph.num_nodes(), dtype=torch.int32), names="seed_nodes")
This does not work
I checked more in Docs and found:
Ok, the graph type may be wrong…
Here is the question, doc:
So confused, need more explanation and help, to convert [my graph] into this [CSC graph]
Grateful
Thanks a lot. That works for me,
gb.from_dglgraph(g, True)
“True” option is necessary
Now a new question comes 
My previous model for [small graph]
output = self.model(graph.ndata["x"], graph.edata["x"], graph)
As far as I observe from [Doc 6.1], my [.ndata[“x”]] should be substituted by [featrue]
Smart code:
datapipe = datapipe.fetch_feature(feature, node_feature_keys=["x"], edge_feature_keys=["x", "y"])
Here is the question: How to construct this feature from my ndata and edata?
Thx
Hi， after some practice over this Dataloader， it seems like everything is okay about my dataloader,
except the edge feature fails to be fetched( return empty list []).
And another question is when I try to substitute dgl graph by blocks in my network.
with graph.local_scope():
An unexpected error occurs : AttributeError: ‘list’ object has no attribute local_scope
As far as I know: The DGL GraphConv modules can accept an element in blocks generated by the data loader as an argument.
I am worrying whether most APIs can accept blocks or not, is that possible and correct if I turn blocks into a DGL graph (a subgraph), and then feed it into my network?
If so, a possible solution would be : # dgl.to_homogeneous does not support DGLBlock MFGs · Issue #6296 · dmlc/dgl · GitHub
In order to fetch edge feature, you need to `gb.from_dglgraph(…, include_original_edge_id=True) first.
Are you replacing graph with data.blocks? Please elaborate more about your question.
For the first question:
# define dataloader
feature = [
gb.OnDiskFeatureData(
domain="node", name="x",
format="numpy", path="./tmp/node_x.npy", in_memory=False),
gb.OnDiskFeatureData(
domain="node", name="y",
format="numpy", path="./tmp/node_y.npy", in_memory=False),
gb.OnDiskFeatureData(
domain="edge", name="x",
format="numpy", path="./tmp/edge.npy", in_memory=False),
]
feature_store = gb.TorchBasedFeatureStore(feature)
sub_graph_sampler = sub_graph_sampler.fetch_feature(feature_store, node_feature_keys=["x", "y", ], edge_feature_keys=["x"])
subgraph_dataloder = gb.DataLoader(sub_graph_sampler)
# fetch blocks, blocks' edge feature and node features
for j, subgraph in enumerate(subgraph_dataloder):
node_x = subgraph.node_features['x'] # works well
node_y = subgraph.node_features['y'] # works well
edge_x = subgraph.node_features['y'] # return an empy listedge_x = subgraph.edge_features['x']?
yes it should be edge ‘x’ not edge ‘y’
Here is the second question:
dgl graph
for graph in dataloader:
node_x = graph.ndata["x"]
node_y = graph.ndata["y"]
edge_x = graph.edata["x"]
loss = model(graph, node_x, node_y, edge_x)
Class GraphNet():
def forward(graph_or_blocks, node_x, node_y, edge_x):
with graph.local_scope(): # Error Here
edge_x = edge_x
...
...
for i, subdataloader in enumerate(dataloader):
for j, subgraph in enumerate(subgraph_dataloder):
node_x = subgraph.node_features['x']
node_y = subgraph.node_features['y']
edge_x = subgraph.edge_features['x']
output = model(subgraph.blocks, node_x, node_y, edge_x)
...
what is dataloader previsouly? It’s GraphDataLoader or dgl.dataloading.DataLoader? How many layers in your model?
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