Train Active

lib/modules/blocks.py

@@ -106,7 +106,7 @@ class ResidualModule(nn.Module):
         self.in_shape = in_shape
         module_paramters.update(in_shape=in_shape)
         self.activation = activation() if activation else lambda x: x
-        self.residual_block = [module_class(**module_paramters) for _ in range(n)]
+        self.residual_block = nn.ModuleList([module_class(**module_paramters) for _ in range(n)])
         assert self.in_shape == self.shape, f'The in_shape: {self.in_shape} - must match the out_shape: {self.shape}.'
 
     def forward(self, x):

lib/modules/utils.py

@@ -133,12 +133,6 @@ class LightningBaseModule(pl.LightningModule, ABC):
     def forward(self, *args, **kwargs):
         raise NotImplementedError
 
-    def validation_step(self, *args, **kwargs):
-        raise NotImplementedError
-
-    def validation_end(self, outputs):
-        raise NotImplementedError
-
     def training_step(self, batch_xy, batch_nb, *args, **kwargs):
         raise NotImplementedError
 
@@ -146,21 +140,7 @@ class LightningBaseModule(pl.LightningModule, ABC):
         raise NotImplementedError
 
     def test_end(self, outputs):
-        from sklearn.metrics import roc_auc_score
-
-        y_scores, y_true = [], []
-        for output in outputs:
-            y_scores.append(output['y_pred'])
-            y_true.append(output['y_true'])
-
-        y_true = torch.cat(y_true, dim=0)
-        # FIXME: What did this do do i need it?
-        # y_true = (y_true != V.HOMOTOPIC).long()
-        y_scores = torch.cat(y_scores, dim=0)
-
-        roc_auc_scores = roc_auc_score(y_true.cpu().numpy(), y_scores.cpu().numpy())
-        print(f'AUC Score: {roc_auc_scores}')
-        return {'roc_auc_scores': roc_auc_scores}
+        raise NotImplementedError
 
     def init_weights(self):
         def _weight_init(m):