Solved problem with pyg loaders

configs/dataset/ZINC.yaml

@@ -3,7 +3,7 @@ _target_: topobenchmarkx.io.load.loaders.GraphLoader
 # USE python train.py dataset.transforms.one_hot_node_degree_features.degrees_fields=x to run this config
 
 defaults:
-  #- transforms/data_manipulations: node_feat_to_float
+  - transforms/data_manipulations: node_degrees
   - transforms/[email protected]_hot_node_degree_features: one_hot_node_degree_features
   - transforms: ${get_default_transform:graph,${model}}
 

configs/dataset/transforms/data_manipulations/one_hot_node_degree_features.yaml

@@ -4,5 +4,5 @@ transform_type: "data manipulation"
 
 degrees_fields: "node_degrees"
 features_fields: "x"
-max_degrees: ${dataset.parameters.max_node_degree}
+max_degree: ${dataset.parameters.max_node_degree}
 

env.sh

@@ -9,7 +9,7 @@ pip install git+https://github.com/pyt-team/TopoEmbedX.git
 
 CUDA="cu121" # if available, select the CUDA version suitable for your system
              # e.g. cpu, cu102, cu111, cu113, cu115
-pip install torch_geometric==2.4.0
+pip install fsspec==2024.5.0
 pip install torch==2.0.1 --extra-index-url https://download.pytorch.org/whl/${CUDA}
 pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.0.1+${CUDA}.html
 pip install torch-cluster -f https://data.pyg.org/whl/torch-2.0.0+${CUDA}.html

topobenchmarkx/io/load/preprocessor.py

@@ -145,43 +145,4 @@ def process(self) -> None:
         self._data_list = None  # Reset cache.
 
         assert isinstance(self._data, torch_geometric.data.Data)
-        self.save(self.data_list, self.processed_paths[0])
-
-    def _process(self):
-        f = osp.join(self.processed_dir, 'pre_transform.pt')
-        if osp.exists(f) and torch.load(f) != _repr(self.pre_transform):
-            warnings.warn(
-                f"The `pre_transform` argument differs from the one used in "
-                f"the pre-processed version of this dataset. If you want to "
-                f"make use of another pre-processing technique, make sure to "
-                f"delete '{self.processed_dir}' first")
-
-        f = osp.join(self.processed_dir, 'pre_filter.pt')
-        if osp.exists(f) and torch.load(f) != _repr(self.pre_filter):
-            warnings.warn(
-                "The `pre_filter` argument differs from the one used in "
-                "the pre-processed version of this dataset. If you want to "
-                "make use of another pre-fitering technique, make sure to "
-                "delete '{self.processed_dir}' first")
-
-        if not self.force_reload and files_exist(self.processed_paths):
-            return
-
-        if self.log and 'pytest' not in sys.modules:
-            print('Processing...', file=sys.stderr)
-
-        makedirs(self.processed_dir)
-        self.process()
-
-        path = osp.join(self.processed_dir, 'pre_transform.pt')
-        torch.save(_repr(self.pre_transform), path)
-        path = osp.join(self.processed_dir, 'pre_filter.pt')
-        torch.save(_repr(self.pre_filter), path)
-
-        if self.log and 'pytest' not in sys.modules:
-            print('Done!', file=sys.stderr)
-
-def _repr(obj: Any) -> str:
-    if obj is None:
-        return 'None'
-    return re.sub('(<.*?)\\s.*(>)', r'\1\2', str(obj))
+        self.save(self.data_list, self.processed_paths[0])

topobenchmarkx/transforms/data_manipulations/one_hot_degree.py

@@ -13,6 +13,7 @@ def __init__(
         self,
         max_degree: int,
         cat: bool = False,
+        **kwargs,
     ) -> None:
         self.max_degree = max_degree
         self.cat = cat

topobenchmarkx/transforms/data_manipulations/one_hot_degree_features.py

@@ -15,7 +15,7 @@ def __init__(self, **kwargs):
         self.type = "one_hot_degree_features"
         self.deg_field = kwargs["degrees_fields"]
         self.features_fields = kwargs["features_fields"]
-        self.transform = OneHotDegree(max_degree=kwargs["max_degrees"])
+        self.transform = OneHotDegree(**kwargs)
 
     def __repr__(self) -> str:
         return f"{self.__class__.__name__}(type={self.type!r}, degrees_field={self.deg_field!r}, features_field={self.features_fields!r})"