Track: Track1; Team name: howyadoin; Model: Neural Sheaf Propagation

configs/model/graph/nsp.yaml

@@ -0,0 +1,47 @@
+_target_: topobench.model.TBModel
+
+model_name: nsp
+model_domain: graph
+
+feature_encoder:
+  _target_: topobench.nn.encoders.${model.feature_encoder.encoder_name}
+  encoder_name: AllCellFeatureEncoder
+  in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}}
+  out_channels: 64
+  proj_dropout: 0.0
+
+backbone:
+  _target_: topobench.nn.backbones.NSPEncoder
+  input_dim: ${model.feature_encoder.out_channels}
+  hidden_dim: ${model.feature_encoder.out_channels} # SWEEP!: [32,64] (not more because feature dim of graph only 15)
+  num_layers: 2 # SWEEP: [2,4] (3 if we have a lot of time)
+  dropout: 0.4 # SWEEP: [0.0, 0.25, 0.5]
+  sheaf_type: bundle # SWEEP: [diag, bundle]
+  d: 3 # no sweep.
+  input_dropout: 0.0 # no sweep.
+
+backbone_wrapper:
+  _target_: topobench.nn.wrappers.${model.backbone_wrapper.wrapper_name}
+  _partial_: true
+  wrapper_name: GNNWrapper
+  out_channels: ${model.feature_encoder.out_channels}
+  residual_connections: false
+  num_cell_dimensions: ${infer_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}}
+
+readout:
+  _target_: topobench.nn.readouts.${model.readout.readout_name}
+  readout_name: MLPReadout
+  num_cell_dimensions: ${infer_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider
+  in_channels: ${model.feature_encoder.out_channels}
+  hidden_layers: [16]
+  out_channels: ${dataset.parameters.num_classes}
+  task_level: ${define_task_level:${dataset.parameters.task_level},${dataset.split_params.learning_setting}} # Handles the edge case of node-inductive task
+  pooling_type: sum
+  # Extra MLP params
+  dropout: 0.2
+  act: "relu"
+  norm: null
+  final_act: null
+
+# compile model for faster training with pytorch 2.0
+compile: false

topobench/evaluator/metrics/__init__.py

@@ -66,7 +66,8 @@ def discover_metrics(cls, package_path: str) -> dict[str, type]:
 
             try:
                 # Use importlib to safely import the module
-                module_name = f"{package_dir.stem}.{file_path.stem}"
+                package_name = package_dir.parent.parent.stem + "." + package_dir.parent.stem + "." + package_dir.stem
+                module_name = f"{package_name}.{file_path.stem}"
                 module = importlib.import_module(module_name)
 
                 # Find all loss classes in the module

topobench/loss/__init__.py

@@ -66,7 +66,8 @@ def discover_losses(cls, package_path: str) -> dict[str, type]:
 
             try:
                 # Use importlib to safely import the module
-                module_name = f"{package_dir.stem}.{file_path.stem}"
+                package_name = package_dir.parent.stem + "." + package_dir.stem
+                module_name = f"{package_name}.{file_path.stem}"
                 module = importlib.import_module(module_name)
 
                 # Find all loss classes in the module

topobench/loss/dataset/__init__.py

@@ -66,7 +66,8 @@ def discover_losses(cls, package_path: str) -> dict[str, type]:
 
             try:
                 # Use importlib to safely import the module
-                module_name = f"{package_dir.stem}.{file_path.stem}"
+                package_name = package_dir.parent.parent.stem + "." + package_dir.parent.stem + "." + package_dir.stem
+                module_name = f"{package_name}.{file_path.stem}"
                 module = importlib.import_module(module_name)
 
                 # Find all loss classes in the module

topobench/loss/model/__init__.py

@@ -66,7 +66,8 @@ def discover_losses(cls, package_path: str) -> dict[str, type]:
 
             try:
                 # Use importlib to safely import the module
-                module_name = f"{package_dir.stem}.{file_path.stem}"
+                package_name = package_dir.parent.parent.stem + "." + package_dir.parent.stem + "." + package_dir.stem
+                module_name = f"{package_name}.{file_path.stem}"
                 module = importlib.import_module(module_name)
 
                 # Find all loss classes in the module