Merge branch 'graphium_3.0' into torchmetrics

.gitignore

@@ -29,6 +29,7 @@ draft/
 scripts-expts/
 sweeps/
 mup/
+loc-*
 
 # Data and predictions
 graphium/data/ZINC_bench_gnn/

env.yml

@@ -12,7 +12,7 @@ dependencies:
   - platformdirs
 
   # scientific
-  - numpy < 2.0 # Issue with wandb
+  - numpy == 1.26.4
   - scipy >=1.4
   - pandas >=1.0
   - scikit-learn
@@ -41,7 +41,7 @@ dependencies:
   - pytorch_scatter >=2.0
 
   # chemistry
-  - rdkit
+  - rdkit == 2024.03.4
   - datamol >=0.10
   - boost # needed by rdkit
 

graphium/data/dataset.py

@@ -45,6 +45,7 @@ def __init__(
         num_edges_tensor=None,
         about: str = "",
         data_path: Optional[Union[str, os.PathLike]] = None,
+        return_smiles: bool = False,
     ):
         r"""
         This class holds the information for the multitask dataset.
@@ -75,6 +76,8 @@ def __init__(
         self.num_edges_tensor = num_edges_tensor
         self.dataset_length = num_nodes_tensor.size(dim=0)
 
+        self.return_smiles = return_smiles
+
         logger.info(f"Dataloading from DISK")
 
     def __len__(self):
@@ -199,6 +202,9 @@ def __getitem__(self, idx):
                 "features": self.featurize_smiles(smiles_str),
             }
 
+        if self.return_smiles:
+            datum["smiles"] = smiles_str
+
         # One of the featurization error handling options returns a string on error,
         # instead of throwing an exception, so assume that the intention is to just skip,
         # instead of crashing.

graphium/graphium_cpp/features.h

@@ -274,4 +274,4 @@ std::tuple<std::vector<at::Tensor>, int64_t, int64_t> featurize_smiles(
 std::unique_ptr<RDKit::RWMol> parse_mol(
     const std::string& smiles_string,
     bool explicit_H,
-    bool ordered = false);
+    bool ordered = true);

graphium/trainer/predictor.py

@@ -563,7 +563,6 @@ def training_step(self, batch: Dict[str, Tensor]) -> Dict[str, Any]:
 
         return step_dict  # Returning the metrics_logs with the loss
 
-
     def validation_step(self, batch: Dict[str, Tensor]) -> Dict[str, Any]:
         return self._general_step(batch=batch, step_name="val")
 
@@ -575,6 +574,12 @@ def _general_epoch_start(self, step_name: Literal["train", "val", "test"]) -> No
         self.epoch_start_time[step_name] = time.time()
         self.mean_time_tracker.reset()
         self.mean_tput_tracker.reset()
+
+    def predict_step(self, batch: Dict[str, Tensor]) -> Dict[str, Any]:
+        preds = self.forward(batch)  # The dictionary of predictions
+        targets_dict = batch.get("labels")
+
+        return preds, targets_dict
 
 
     def _general_epoch_end(self, step_name: Literal["train", "val", "test"]) -> Dict[str, Tensor]:
@@ -628,12 +633,12 @@ def on_validation_epoch_end(self) -> None:
         self._general_epoch_end(step_name="val")
         return super().on_validation_epoch_end()
 
-
     def on_test_epoch_start(self) -> None:
         self._general_epoch_start(step_name="test")
         return super().on_test_epoch_start()
 
     def on_test_epoch_end(self) -> None:
+
         self._general_epoch_end(step_name="test")
         return super().on_test_epoch_end()