fix(task/seg): training script changes for compat w/ newer version of lightning

tasks/face_segmentation/train.py

@@ -49,7 +49,7 @@ if __name__ == '__main__':
     ]
 
     trainer = Trainer(
-        default_root_dir=str(args.output_dir), gpus=1, 
+        default_root_dir=str(args.output_dir), accelerator='gpu', 
         val_check_interval=args.val_check_interval,
         max_epochs=args.epochs,
         callbacks=callbacks

tasks/face_segmentation/training/pl_trainer.py

@@ -40,52 +40,32 @@ class SegmentationTrainingLoop(pl.LightningModule):
             pred_classes.cpu(), mask.cpu(), self.num_classes, self.ignore_mask_value
         )
 
-        return {
-            'loss': loss,
-            'confusion_matrix': confusion_matrix
+        iou_dict = get_iou(confusion_matrix)
+        f1_dict = get_f1_scores(confusion_matrix)
+
+        results = {
+            f'{stage}_loss': loss,
+            f'{stage}_mIOU': iou_dict['mIOU'],
+            f'{stage}_macro_f1': f1_dict['macro_f1'],
+            f'{stage}_weighted_f1': f1_dict['weighted_f1']
         }
 
+        return results
+
     def training_step(self, batch, batch_idx):
         results = self.shared_step(batch, stage='train')
-        self.log_dict({'training_loss': results['loss']}, sync_dist=True)
+        self.log_dict(results, sync_dist=True, on_step=True, on_epoch=True)
+        return results['train_loss']
 
+    def validation_step(self, batch, batch_idx):
+        results = self.shared_step(batch, stage='validation')
+        self.log_dict(results, sync_dist=True, on_step=False, on_epoch=True)
         return results
 
     def predict(self, image):
         with torch.no_grad():
             return self.model.predict(image)
 
-    def validation_step(self, batch, batch_idx):
-        results = self.shared_step(batch, stage='validation')
-        return results
-
-    def validation_epoch_end(self, outputs):
-        self.shared_epoch_end(outputs, stage='validation')
-
-    def shared_epoch_end(self, outputs, stage):
-        confusion_matrix = sum([x['confusion_matrix'] for x in outputs])
-        avg_loss = torch.tensor([x['loss'] for x in outputs]).mean()
-
-        iou_dict = get_iou(confusion_matrix)
-        f1_dict = get_f1_scores(confusion_matrix)
-
-        results = {
-           f'{stage}_loss': avg_loss,
-           f'{stage}_mIOU': iou_dict['mIOU'],
-           f'{stage}_macro_f1': f1_dict['macro_f1'],
-           f'{stage}_weighted_f1': f1_dict['weighted_f1']
-        }
-
-        self.log_dict(results, sync_dist=True)
-        return results
-
     def configure_optimizers(self):
         optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)
-        scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.973435286)
-
-        scheduler = {
-            'scheduler': scheduler,
-            'interval': 'epoch'
-        }
-
-        return [optimizer], [scheduler]
+        return [optimizer]