make gatv2 as default

Author: aditya0by0

README.md

@@ -94,7 +94,7 @@ Among all the connection schemes we evaluated, this configuration delivered the
 Below is the command for the model and data configuration that achieved the best classification performance using augmented graphs.
 
 ```bash
-python -m chebai fit --trainer=configs/training/default_trainer.yml --trainer.logger=configs/training/wandb_logger.yml --model=../python-chebai-graph/configs/model/gat_aug_amgpool.yml --model.train_metrics=configs/metrics/micro-macro-f1.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --model.val_metrics=configs/metrics/micro-macro-f1.yml --model.config.v2=True --data=../python-chebai-graph/configs/data/chebi50_aug_prop_as_per_node.yml --data.init_args.batch_size=128 --trainer.accumulate_grad_batches=4 --data.init_args.num_workers=10 --model.pass_loss_kwargs=false --data.init_args.chebi_version=241 --trainer.min_epochs=200 --trainer.max_epochs=200 --model.criterion=configs/loss/bce.yml --trainer.logger.init_args.name=gatv2_amg_s0
+python -m chebai fit --trainer=configs/training/default_trainer.yml --trainer.logger=configs/training/wandb_logger.yml --model=../python-chebai-graph/configs/model/gat_aug_amgpool.yml --model.train_metrics=configs/metrics/micro-macro-f1.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --model.val_metrics=configs/metrics/micro-macro-f1.yml --data=../python-chebai-graph/configs/data/chebi50_aug_prop_as_per_node.yml --data.init_args.batch_size=128 --trainer.accumulate_grad_batches=4 --data.init_args.num_workers=10 --model.pass_loss_kwargs=false --data.init_args.chebi_version=241 --trainer.min_epochs=200 --trainer.max_epochs=200 --model.criterion=configs/loss/bce.yml --trainer.logger.init_args.name=gatv2_amg_s0
 ```
 
 ### Model Hyperparameters
@@ -104,7 +104,7 @@ python -m chebai fit --trainer=configs/training/default_trainer.yml --trainer.lo
 To use a GAT-based model, choose **one** of the following configs:
 
 - **Standard Pooling**: `--model=../python-chebai-graph/configs/model/gat.yml`
-   > Standard pooling sums the learned representations from all the nodes to produce a single representation which is used for classification.  
+   > Standard pooling sums the learned representations from all the nodes to produce a single representation which is used for classification.
 
 - **Atom-Augmented Node Pooling**: `--model=../python-chebai-graph/configs/model/gat_aug_aagpool.yml`
    > With this pooling stratergy, the learned representations are first separated into **two distinct sets**: those from atom nodes and those from all artificial nodes (both functional groups and the graph node). The representations within each set are aggregated separately (using summation) to yield two distinct single vectors. These two resulting vectors are then concatenated before being passed to the classification layer.
@@ -117,9 +117,13 @@ To use a GAT-based model, choose **one** of the following configs:
 - **Number of message-passing layers**: `--model.config.num_layers=5`        (default: 4)
 - **Attention heads**: `--model.config.heads=4`             (default: 8)
   > **Note**: The number of heads should be divisible by the output channels (or hidden channels if output channels are not specified).
-- **Use GATv2**: `--model.config.v2=True`             (default: False)
-  > **Note**: GATv2 addresses the limitation of static attention in GAT by introducing a dynamic attention mechanism. For further details, please refer to the [original GATv2 paper](https://arxiv.org/abs/2105.14491).
-  
+
+- **To Use different GAT versions**:
+    - **Use GAT**: `--model.config.v2=False`
+
+    - **Use GATv2**: `--model.config.v2=True`             (__default__)
+      > **Note**: GATv2 addresses the limitation of static attention in GAT by introducing a dynamic attention mechanism. For further details, please refer to the [original GATv2 paper](https://arxiv.org/abs/2105.14491).
+
 #### **ResGated Architecture**
 
 To use a ResGated GNN model, choose **one** of the following configs:

configs/model/gat.yml

@@ -9,7 +9,6 @@ init_args:
     num_layers: 4
     edge_dim: 7 # number of bond properties
     heads: 8  # the number of heads should be divisible by output channels (hidden channels if output channel not given)
-    v2: False  # set True to use `torch_geometric.nn.conv.GATv2Conv` convolution layers, default is GATConv
-    dropout: 0
+    v2: True  # This uses `torch_geometric.nn.conv.GATv2Conv` convolution layers, set False to use `GATConv`
   n_molecule_properties: 0
   n_linear_layers: 1

configs/model/gat_aug_aapool.yml

@@ -9,7 +9,6 @@ init_args:
     num_layers: 4
     edge_dim: 11 # number of bond properties
     heads: 8  # the number of heads should be divisible by output channels (hidden channels if output channel not given)
-    v2: False  # set True to use `torch_geometric.nn.conv.GATv2Conv` convolution layers, default is GATConv
-    dropout: 0
+    v2: True  # This uses `torch_geometric.nn.conv.GATv2Conv` convolution layers, set False to use `GATConv`
   n_molecule_properties: 0
   n_linear_layers: 1

configs/model/gat_aug_amgpool.yml

@@ -9,7 +9,7 @@ init_args:
     num_layers: 4
     edge_dim: 11 # number of bond properties
     heads: 8  # the number of heads should be divisible by output channels (hidden channels if output channel not given)
-    v2: True  # set True to use `torch_geometric.nn.conv.GATv2Conv` convolution layers, default is GATConv
+    v2: True  # This uses `torch_geometric.nn.conv.GATv2Conv` convolution layers, set False to use `GATConv`
     dropout: 0
   n_molecule_properties: 0
   n_linear_layers: 1