general readout

geometric-intelligence · May 11, 2024 · cac20f4 · cac20f4
1 parent 332edef
commit cac20f4
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diff --git a/configs/dataset/PROTEINS_TU.yaml b/configs/dataset/PROTEINS_TU.yaml
@@ -19,7 +19,7 @@ parameters:
   monitor_metric: accuracy
   task_level: graph
   data_seed: 9
-  split_type: random #'k-fold' # either "k-fold" or "random" strategies
+  split_type: k-fold #'k-fold' # either "k-fold" or "random" strategies
   k: 10 # for "k-fold" Cross-Validation
   train_prop: 0.5 # for "random" strategy splitting
 

diff --git a/configs/dataset/transforms/graph2simplicial_lifting/simplicial_clique.yaml b/configs/dataset/transforms/graph2simplicial_lifting/simplicial_clique.yaml
@@ -1,7 +1,7 @@
 _target_: topobenchmarkx.transforms.data_transform.DataTransform
 transform_type: 'lifting'
 transform_name: "SimplicialCliqueLifting"
-complex_dim: ${oc.select:dataset.parameters.max_dim_if_lifted,2}
+complex_dim: ${oc.select:dataset.parameters.max_dim_if_lifted,3}
 preserve_edge_attr: ${oc.select:dataset.parameters.preserve_edge_attr_if_lifted,False}
-signed: True
+signed: False
 feature_lifting: projection
diff --git a/configs/model/cell/can.yaml b/configs/model/cell/can.yaml
@@ -14,26 +14,35 @@ backbone:
   in_channels_1: ${model.feature_encoder.out_channels}
   out_channels: ${model.feature_encoder.out_channels}
   dropout: 0.0
-  heads: 4 # For now we stuck to out_channels//heads
+  heads: 1 # For now we stuck to out_channels//heads, keep heads = 1 
   concat: True
   skip_connection: True
-  n_layers: 4
+  n_layers: 1
   att_lift: False
 
-loss:
-  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
-  task: ${dataset.parameters.task}
-  loss_type: ${dataset.parameters.loss_type}
+backbone_wrapper:
+  _target_: topobenchmarkx.models.wrappers.default_wrapper.CANWrapper
+  _partial_: true
+  out_channels: ${model.feature_encoder.out_channels}
+  num_cell_dimensions: ${infere_list_length:${model.feature_encoder.selected_dimensions}}
 
 readout:
-  _target_: topobenchmarkx.models.readouts.default_readouts.GNNBatchReadOut
+  _target_: topobenchmarkx.models.readouts.readout.AbstractReadOut
+  readout_name: PropagateSignalDown #  Use <None> in case readout is not needed
+  hidden_dim: ${model.feature_encoder.out_channels}
+  num_cell_dimensions: ${infere_list_length:${model.feature_encoder.selected_dimensions}}
+
+head_model:
+  _target_: topobenchmarkx.models.head_model.models.DefaultHead
   task_level: ${dataset.parameters.task_level}
   in_channels: ${parameter_multiplication:${model.backbone.out_channels},${model.backbone.heads}}
   out_channels: ${dataset.parameters.num_classes}
+  pooling_type: sum  
 
-backbone_wrapper:
-  _target_: topobenchmarkx.models.wrappers.default_wrapper.CANWrapper
-  _partial_: true
+loss:
+  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
+  task: ${dataset.parameters.task}
+  loss_type: ${dataset.parameters.loss_type}
 
 optimizer:
   _target_: torch.optim.Adam

diff --git a/configs/model/cell/ccxn.yaml b/configs/model/cell/ccxn.yaml
@@ -16,20 +16,29 @@ backbone:
 backbone_additional_params:
   hidden_channels: ${model.feature_encoder.out_channels}
 
-loss:
-  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
-  task: ${dataset.parameters.task}
-  loss_type: ${dataset.parameters.loss_type}
+backbone_wrapper:
+  _target_: topobenchmarkx.models.wrappers.default_wrapper.CCXNWrapper
+  _partial_: true
+  out_channels: ${model.feature_encoder.out_channels}
+  num_cell_dimensions: ${infere_list_length:${model.feature_encoder.in_channels}}
 
 readout:
-  _target_: topobenchmarkx.models.readouts.default_readouts.GNNBatchReadOut
+  _target_: topobenchmarkx.models.readouts.readout.AbstractReadOut
+  readout_name: PropagateSignalDown #  Use <None> in case readout is not needed
+  hidden_dim: ${model.feature_encoder.out_channels}
+  num_cell_dimensions: ${infere_list_length:${model.feature_encoder.in_channels}}
+
+head_model:
+  _target_: topobenchmarkx.models.head_model.models.DefaultHead
   task_level: ${dataset.parameters.task_level}
-  in_channels: ${model.backbone_additional_params.hidden_channels}
+  in_channels: ${model.feature_encoder.out_channels}
   out_channels: ${dataset.parameters.num_classes}
+  pooling_type: sum
 
-backbone_wrapper:
-  _target_: topobenchmarkx.models.wrappers.default_wrapper.CCXNWrapper
-  _partial_: true
+loss:
+  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
+  task: ${dataset.parameters.task}
+  loss_type: ${dataset.parameters.loss_type}
 
 optimizer:
   _target_: torch.optim.Adam

diff --git a/configs/model/cell/cwn.yaml b/configs/model/cell/cwn.yaml
@@ -14,22 +14,30 @@ backbone:
   hid_channels: ${model.feature_encoder.out_channels}
   n_layers: 1
 
-loss:
-  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
-  task: ${dataset.parameters.task}
-  loss_type: ${dataset.parameters.loss_type}
+backbone_wrapper:
+  _target_: topobenchmarkx.models.wrappers.default_wrapper.CWNWrapper
+  _partial_: true
+  out_channels: ${model.feature_encoder.out_channels}
+  num_cell_dimensions: ${infere_list_length:${model.feature_encoder.in_channels}}
+
 
 readout:
-  _target_: topobenchmarkx.models.readouts.default_readouts.GNNBatchReadOut
+  _target_: topobenchmarkx.models.readouts.readout.AbstractReadOut
+  readout_name: PropagateSignalDown #  Use <None> in case readout is not needed
+  hidden_dim: ${model.feature_encoder.out_channels}
+  num_cell_dimensions: ${infere_list_length:${model.feature_encoder.in_channels}}
+
+head_model:
+  _target_: topobenchmarkx.models.head_model.models.DefaultHead
   task_level: ${dataset.parameters.task_level}
-  in_channels: ${model.backbone.hid_channels}
+  in_channels: ${model.feature_encoder.out_channels}
   out_channels: ${dataset.parameters.num_classes}
+  pooling_type: sum
 
-backbone_wrapper:
-  _target_: topobenchmarkx.models.wrappers.default_wrapper.CWNWrapper
-  _partial_: true
-  out_channels: ${model.feature_encoder.out_channels}
-  normalize_outuputs: False
+loss:
+  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
+  task: ${dataset.parameters.task}
+  loss_type: ${dataset.parameters.loss_type}
 
 optimizer:
   _target_: torch.optim.Adam

diff --git a/configs/model/cell/cwn_dcm.yaml b/configs/model/cell/cwn_dcm.yaml
@@ -5,27 +5,39 @@ feature_encoder:
   in_channels: ${infer_in_channels:${dataset}} #${dataset.parameters.num_features}
   out_channels: 32
   proj_dropout: 0.0
+  selected_dimensions:
+    - 0
+    - 1
 
 backbone:
   _target_: custom_models.cell.cwn_dcm.CWNDCM
   in_channels: ${model.feature_encoder.out_channels}
   n_layers: 1
   dropout: 0.0
 
-loss:
-  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
-  task: ${dataset.parameters.task}
-  loss_type: ${dataset.parameters.loss_type}
+backbone_wrapper:
+  _target_: topobenchmarkx.models.wrappers.default_wrapper.CWNDCMWrapper
+  _partial_: true
+  out_channels: ${model.feature_encoder.out_channels}
+  num_cell_dimensions: ${infere_list_length:${model.feature_encoder.selected_dimensions}}
 
 readout:
-  _target_: topobenchmarkx.models.readouts.default_readouts.GNNBatchReadOut
+  _target_: topobenchmarkx.models.readouts.readout.AbstractReadOut
+  readout_name: PropagateSignalDown #  Use <None> in case readout is not needed
+  hidden_dim: ${model.feature_encoder.out_channels}
+  num_cell_dimensions: ${infere_list_length:${model.feature_encoder.selected_dimensions}}
+
+head_model:
+  _target_: topobenchmarkx.models.head_model.models.DefaultHead
   task_level: ${dataset.parameters.task_level}
-  in_channels: ${model.backbone.in_channels}
+  in_channels: ${model.feature_encoder.out_channels}
   out_channels: ${dataset.parameters.num_classes}
+  pooling_type: sum
 
-backbone_wrapper:
-  _target_: topobenchmarkx.models.wrappers.default_wrapper.CWNDCMWrapper
-  _partial_: true
+loss:
+  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
+  task: ${dataset.parameters.task}
+  loss_type: ${dataset.parameters.loss_type}
 
 optimizer:
   _target_: torch.optim.Adam

diff --git a/configs/model/graph/gat.yaml b/configs/model/graph/gat.yaml
@@ -2,7 +2,7 @@ _target_: topobenchmarkx.models.network_module.NetworkModule
 
 feature_encoder:
   _target_: topobenchmarkx.models.encoders.default_encoders.BaseFeatureEncoder
-  in_channels: ${infer_in_channels:${dataset}} #${dataset.parameters.num_features}
+  in_channels: ${infer_in_channels:${dataset}} 
   out_channels: 32
 
 backbone:
@@ -16,29 +16,30 @@ backbone:
   heads: 4
   concat: true
 
-loss:
-  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
-  task: ${dataset.parameters.task}
-  loss_type: ${dataset.parameters.loss_type}
-
-# evaluator:
-#   _target_: topobenchmarkx.evaluators.evaluator.Evaluator
-#   metrics: [acc, rocauc]
+backbone_wrapper:
+  _target_: topobenchmarkx.models.wrappers.default_wrapper.GNNWrapper
+  _partial_: true
+  out_channels: ${model.feature_encoder.out_channels}
+  num_cell_dimensions: ${infere_list_length:${model.feature_encoder.in_channels}}
 
 readout:
-  _target_: topobenchmarkx.models.readouts.default_readouts.GNNBatchReadOut
+  _target_: topobenchmarkx.models.readouts.readout.AbstractReadOut
+  readout_name: PropagateSignalDown #  Use <None> in case readout is not needed
+  hidden_dim: ${model.feature_encoder.out_channels}
+  num_cell_dimensions: ${infere_list_length:${model.feature_encoder.in_channels}}
+
+head_model:
+  _target_: topobenchmarkx.models.head_model.models.DefaultHead
   task_level: ${dataset.parameters.task_level}
-  in_channels: ${model.backbone.hidden_channels}
+  in_channels: ${model.feature_encoder.out_channels}
   out_channels: ${dataset.parameters.num_classes}
-
-# readout_workaround:
-#   _target_: topobenchmarkx.models.readout_workaround.ReadOutWorkaround
-#   backbone_outputs: ["x_0"]
+  pooling_type: sum
 
 
-backbone_wrapper:
-  _target_: topobenchmarkx.models.wrappers.default_wrapper.GNNWrapper
-  _partial_: true
+loss:
+  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
+  task: ${dataset.parameters.task}
+  loss_type: ${dataset.parameters.loss_type}
 
 optimizer:
   _target_: torch.optim.Adam

diff --git a/configs/model/graph/gcn.yaml b/configs/model/graph/gcn.yaml
@@ -13,21 +13,29 @@ backbone:
   dropout: 0.0
   act: relu
 
-loss:
-  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
-  task: ${dataset.parameters.task}
-  loss_type: ${dataset.parameters.loss_type}
+backbone_wrapper:
+  _target_: topobenchmarkx.models.wrappers.default_wrapper.GNNWrapper
+  _partial_: true
+  out_channels: ${model.feature_encoder.out_channels}
+  num_cell_dimensions: ${infere_list_length:${model.feature_encoder.in_channels}}
 
 readout:
-  _target_: topobenchmarkx.models.readouts.default_readouts.GNNBatchReadOut
+  _target_: topobenchmarkx.models.readouts.readout.AbstractReadOut
+  readout_name: PropagateSignalDown #  Use <None> in case readout is not needed
+  hidden_dim: ${model.feature_encoder.out_channels}
+  num_cell_dimensions: ${infere_list_length:${model.feature_encoder.in_channels}}
+
+head_model:
+  _target_: topobenchmarkx.models.head_model.models.DefaultHead
   task_level: ${dataset.parameters.task_level}
-  in_channels: ${model.backbone.hidden_channels}
+  in_channels: ${model.feature_encoder.out_channels}
   out_channels: ${dataset.parameters.num_classes}
+  pooling_type: sum
 
-backbone_wrapper:
-  _target_: topobenchmarkx.models.wrappers.default_wrapper.GNNWrapper
-  _partial_: true
-  out_channels: 64
+loss:
+  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
+  task: ${dataset.parameters.task}
+  loss_type: ${dataset.parameters.loss_type}
 
 optimizer:
   _target_: torch.optim.Adam

diff --git a/configs/model/graph/gin.yaml b/configs/model/graph/gin.yaml
@@ -13,29 +13,30 @@ backbone:
   dropout: 0.0
   act: relu
 
-loss:
-  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
-  task: ${dataset.parameters.task}
-  loss_type: ${dataset.parameters.loss_type}
-
-# evaluator:
-#   _target_: topobenchmarkx.evaluators.evaluator.Evaluator
-#   metrics: [acc, rocauc]
+backbone_wrapper:
+  _target_: topobenchmarkx.models.wrappers.default_wrapper.GNNWrapper
+  _partial_: true
+  out_channels: ${model.feature_encoder.out_channels}
+  num_cell_dimensions: ${infere_list_length:${model.feature_encoder.in_channels}}
 
 readout:
-  _target_: topobenchmarkx.models.readouts.default_readouts.GNNBatchReadOut
+  _target_: topobenchmarkx.models.readouts.readout.AbstractReadOut
+  readout_name: PropagateSignalDown #  Use <None> in case readout is not needed
+  hidden_dim: ${model.feature_encoder.out_channels}
+  num_cell_dimensions: ${infere_list_length:${model.feature_encoder.in_channels}}
+
+head_model:
+  _target_: topobenchmarkx.models.head_model.models.DefaultHead
   task_level: ${dataset.parameters.task_level}
-  in_channels: ${model.backbone.hidden_channels}
+  in_channels: ${model.feature_encoder.out_channels}
   out_channels: ${dataset.parameters.num_classes}
-
-# readout_workaround:
-#   _target_: topobenchmarkx.models.readout_workaround.ReadOutWorkaround
-#   backbone_outputs: ["x_0"]
+  pooling_type: sum
 
 
-backbone_wrapper:
-  _target_: topobenchmarkx.models.wrappers.default_wrapper.GNNWrapper
-  _partial_: true
+loss:
+  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
+  task: ${dataset.parameters.task}
+  loss_type: ${dataset.parameters.loss_type}
 
 optimizer:
   _target_: torch.optim.Adam

diff --git a/configs/model/hypergraph/alldeepset.yaml b/configs/model/hypergraph/alldeepset.yaml
@@ -23,20 +23,29 @@ backbone:
     _partial_: true
     #num_features: ${model.backbone.hidden_channels}
 
-loss:
-  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
-  task: ${dataset.parameters.task}
-  loss_type: ${dataset.parameters.loss_type}
+backbone_wrapper:
+  _target_: topobenchmarkx.models.wrappers.default_wrapper.HypergraphWrapper
+  _partial_: true
+  out_channels: ${model.feature_encoder.out_channels}
+  num_cell_dimensions: ${infere_list_length:${model.feature_encoder.in_channels}}
 
 readout:
-  _target_: topobenchmarkx.models.readouts.default_readouts.GNNBatchReadOut
+  _target_: topobenchmarkx.models.readouts.readout.AbstractReadOut
+  readout_name: None #  Use <None> in case readout is not needed
+  hidden_dim: None
+  num_cell_dimensions: None
+
+head_model:
+  _target_: topobenchmarkx.models.head_model.models.DefaultHead
   task_level: ${dataset.parameters.task_level}
-  in_channels: ${model.backbone.hidden_channels}
+  in_channels: ${model.feature_encoder.out_channels}
   out_channels: ${dataset.parameters.num_classes}
+  pooling_type: sum
 
-backbone_wrapper:
-  _target_: topobenchmarkx.models.wrappers.default_wrapper.HypergraphWrapper
-  _partial_: true
+loss:
+  _target_: topobenchmarkx.models.losses.loss.DefaultLoss
+  task: ${dataset.parameters.task}
+  loss_type: ${dataset.parameters.loss_type}
 
 optimizer:
   _target_: torch.optim.Adam