ENH: Add Model class for model training and validation

src/data.py

@@ -352,6 +352,7 @@ def load_datapoints(atoms, process_dict):
     #                      ])
     print(dataset[0][-1].x)
     print(dataset.df_name_idx.head())
+    print(dataset[0][-1].name)
 
     # Create datapoint
     atoms = read(data_root_path / "Pt_3_Rh_9_-7-7-S.cif")

src/models.py

@@ -110,7 +110,7 @@ def init_conv_layers(self):
                     gnn.CGConv(
                         channels=self.conv_size[i],
                         dim=self.num_edge_features[i],
-                        batch_norm=True,
+                        batch_norm=False,
                     ),
                     nn.LeakyReLU(inplace=True),
                 ]

src/samplers.py

@@ -67,9 +67,11 @@ def create_samplers(self, sample_config):
         randomizer.shuffle(idx_array)
 
         # Get indices
-        train_size = int(np.ceil(sample_config["train"] * self.dataset_size))
+        if sample_config["train"] < 1.:
+            train_size = int(np.ceil(sample_config["train"] * self.dataset_size))
         train_idx = idx_array[:train_size]
-        val_size = int(np.ceil(sample_config["val"] * self.dataset_size))
+        if sample_config["val"] < 1.:
+            val_size = int(np.floor(sample_config["val"] * self.dataset_size))
         val_idx = idx_array[train_size : train_size + val_size]
         test_idx = idx_array[train_size + val_size :]