HPC update

Author: Roman Joeres

ext_baselines/sweettalk.py

@@ -182,11 +182,11 @@ def main(base: Path, task: str):
     else:
         raise ValueError(f"Unknown task {task}")
 
-    # data_config = get_dataset(config, "/scratch/chair_kalinina/s8rojoer/GIFFLAR/data_new_256")
-    data_config = get_dataset(config, "/scratch/SCRATCH_SAS/roman/Gothenburg/GIFFLAR/data_new_256")
+    data_config = get_dataset(config, "/scratch/chair_kalinina/s8rojoer/GIFFLAR/data_new_256")
+    # data_config = get_dataset(config, "/scratch/SCRATCH_SAS/roman/Gothenburg/GIFFLAR/data_new_256")
     datamodule = DownstreamGDM(
-        # root="/scratch/chair_kalinina/s8rojoer/GIFFLAR/data_new_256",
-        root="/scratch/SCRATCH_SAS/roman/Gothenburg/GIFFLAR/data_new_256", 
+        root="/scratch/chair_kalinina/s8rojoer/GIFFLAR/data_new_256",
+        # root="/scratch/SCRATCH_SAS/roman/Gothenburg/GIFFLAR/data_new_256", 
         filename=data_config["filepath"], 
         hash_code="e2301aa9",
         batch_size=64, 

gifflar/data/modules.py

@@ -31,7 +31,7 @@ def __init__(
         """
         super().__init__()
         self.batch_size = batch_size
-        self.num_workers = 64  # 32  # num_workers
+        self.num_workers = 32  # num_workers
 
     def train_dataloader(self) -> DataLoader:
         """

gifflar/model/baselines/rgcn.py

@@ -13,8 +13,8 @@ def __init__(
             self,
             feat_dim: int,
             hidden_dim: int,
-            output_dim: int,
-            task: Literal["regression", "classification", "multilabel", "spectrum"],
+            output_dim: int = 1,
+            task: Literal["regression", "classification", "multilabel", "spectrum"] | None = None,
             num_layers: int = 3,
             batch_size: int = 32,
             pre_transform_args: Optional[dict] = None,
@@ -57,7 +57,9 @@ def forward(self, batch: HeteroDataBatch) -> dict[str, torch.Tensor]:
             node_embeds = conv(node_embeds, batch["rgcn_edge_index"], batch["rgcn_edge_type"])
 
         graph_embed = self.pooling(node_embeds, batch["rgcn_batch"])
-        pred = self.head(graph_embed)
+        pred = None
+        if self.task is not None:
+            pred = self.head(graph_embed)
         return {
                 "node_embed": node_embeds,
             "graph_embed": graph_embed,