fixed TransformerGen init

gfun/generalizedFunnelling.py

@@ -59,8 +59,8 @@ class GeneralizedFunnelling:
         # Textual Transformer VGF params ----------
         self.textual_trf_name = textual_transformer_name
         self.epochs = epochs
-        self.txt_trf_lr = textual_lr
+        self.textual_trf_lr = textual_lr
-        self.vis_trf_lr = visual_lr
+        self.textual_scheduler = "ReduceLROnPlateau"
         self.batch_size_trf = batch_size
         self.eval_batch_size_trf = eval_batch_size
         self.max_length = max_length
@@ -70,6 +70,8 @@ class GeneralizedFunnelling:
         self.device = device
         # Visual Transformer VGF params ----------
         self.visual_trf_name = visual_transformer_name
+        self.visual_trf_lr = visual_lr
+        self.visual_scheduler = "ReduceLROnPlateau"
         # Metaclassifier params ------------
         self.optimc = optimc
         # -------------------
@@ -115,7 +117,7 @@ class GeneralizedFunnelling:
                 self.attn_aggregator = AttentionAggregator(
                     embed_dim=self.get_attn_agg_dim(attn_stacking_type=attn_stacking),
                     out_dim=self.num_labels,
-                    lr=self.txt_trf_lr,
+                    lr=self.textual_trf_lr,
                     patience=self.patience,
                     num_heads=1,
                     device=self.device,
@@ -149,7 +151,8 @@ class GeneralizedFunnelling:
             transformer_vgf = TextualTransformerGen(
                 dataset_name=self.dataset_name,
                 model_name=self.textual_trf_name,
-                lr=self.txt_trf_lr,
+                lr=self.textual_trf_lr,
+                scheduler=self.textual_scheduler,
                 epochs=self.epochs,
                 batch_size=self.batch_size_trf,
                 batch_size_eval=self.eval_batch_size_trf,
@@ -168,7 +171,8 @@ class GeneralizedFunnelling:
             visual_trasformer_vgf = VisualTransformerGen(
                 dataset_name=self.dataset_name,
                 model_name="vit",
-                lr=self.vis_trf_lr,
+                lr=self.visual_trf_lr,
+                scheduler=self.visual_scheduler,
                 epochs=self.epochs,
                 batch_size=self.batch_size_trf,
                 batch_size_eval=self.eval_batch_size_trf,
@@ -185,7 +189,7 @@ class GeneralizedFunnelling:
             self.attn_aggregator = AttentionAggregator(
                 embed_dim=self.get_attn_agg_dim(attn_stacking_type=attn_stacking),
                 out_dim=self.num_labels,
-                lr=self.txt_trf_lr,
+                lr=self.textual_trf_lr,
                 patience=self.patience,
                 num_heads=1,
                 device=self.device,
@@ -346,7 +350,7 @@ class GeneralizedFunnelling:
                 pickle.dump(self.metaclassifier, f)
         return
 
-    def save_first_tier_learners(self):
+    def save_first_tier_learners(self, model_id):
         for vgf in self.first_tier_learners:
             vgf.save_vgf(model_id=self._model_id)
         return self

gfun/vgfs/textualTransformerGen.py

@@ -70,22 +70,24 @@ class TextualTransformerGen(ViewGen, TransformerGen):
         verbose=False,
         patience=5,
         classification_type="multilabel",
+        scheduler="ReduceLROnPlateau",
     ):
         super().__init__(
             self._validate_model_name(model_name),
             dataset_name,
-            epochs,
+            epochs=epochs,
-            lr,
+            lr=lr,
-            batch_size,
+            scheduler=scheduler,
-            batch_size_eval,
+            batch_size=batch_size,
-            max_length,
+            batch_size_eval=batch_size_eval,
-            print_steps,
+            device=device,
-            device,
+            evaluate_step=evaluate_step,
-            probabilistic,
+            patience=patience,
-            n_jobs,
+            probabilistic=probabilistic,
-            evaluate_step,
+            max_length=max_length,
-            verbose,
+            print_steps=print_steps,
-            patience,
+            n_jobs=n_jobs,
+            verbose=verbose,
         )
         self.clf_type = classification_type
         self.fitted = False
@@ -187,8 +189,7 @@ class TextualTransformerGen(ViewGen, TransformerGen):
             vgf_name="textual_trf",
             classification_type=self.clf_type,
             n_jobs=self.n_jobs,
-            scheduler_name="ReduceLROnPlateau",
+            scheduler_name=self.scheduler,
-            # scheduler_name=None,
         )
         trainer.train(
             train_dataloader=tra_dataloader,

gfun/vgfs/transformerGen.py

@@ -26,6 +26,7 @@ class TransformerGen:
         evaluate_step=10,
         verbose=False,
         patience=5,
+        scheduler=None,
     ):
         self.model_name = model_name
         self.dataset_name = dataset_name
@@ -46,6 +47,7 @@ class TransformerGen:
         self.verbose = verbose
         self.patience = patience
         self.datasets = {}
+        self.scheduler = scheduler
         self.feature2posterior_projector = (
             self.make_probabilistic() if probabilistic else None
         )
@@ -101,6 +103,7 @@ class TransformerGen:
             "dataset_name": self.dataset_name,
             "epochs": self.epochs,
             "lr": self.lr,
+            "scheduler": self.scheduler,
             "batch_size": self.batch_size,
             "batch_size_eval": self.batch_size_eval,
             "max_length": self.max_length,
@@ -111,4 +114,4 @@ class TransformerGen:
             "evaluate_step": self.evaluate_step,
             "verbose": self.verbose,
             "patience": self.patience,
-        }
+        }

gfun/vgfs/visualTransformerGen.py

@@ -20,6 +20,7 @@ class VisualTransformerGen(ViewGen, TransformerGen):
         model_name,
         dataset_name,
         lr=1e-5,
+        scheduler="ReduceLROnPlateau",
         epochs=10,
         batch_size=32,
         batch_size_eval=128,
@@ -32,8 +33,9 @@ class VisualTransformerGen(ViewGen, TransformerGen):
         super().__init__(
             model_name,
             dataset_name,
-            lr=lr,
             epochs=epochs,
+            lr=lr,
+            scheduler=scheduler,
             batch_size=batch_size,
             batch_size_eval=batch_size_eval,
             device=device,