fix finetuning of pos embedding

neural_networks/train_nn.py

@@ -153,7 +153,7 @@ def __init__(
         use_lora: bool = False,
         alpha: float = 16.0,
         rank: int = 16,
-        tune_pos_embed: bool = False,
+        pos_embed: bool = False,
     ):
         super().__init__()
 
@@ -170,7 +170,7 @@ def __init__(
             "use_lora": use_lora,
             "rank": rank,
             "alpha": alpha,
-            "tune_pos_embed": tune_pos_embed,
+            "pos_embed": pos_embed,
         }
 
         if lift == "stack":
@@ -194,6 +194,13 @@ def __init__(
                 model_name, get_classifier_f, use_lora=use_lora, alpha=alpha, rank=rank
             )
             # self.classifier = get_classifier_f(n_features=num_features)
+            if "zeros" in self.kwargs["pos_embed"]:
+                self.dino.encoder.pos_embed[:, 1:, :] = torch.zeros_like(
+                    self.dino.encoder.pos_embed[:, 1:, :]
+                )
+            self.dino.encoder.pos_embed.requires_grad = (
+                True if "fine-tune" in self.kwargs["pos_embed"] else False
+            )
             self.forward = self.dino_forward
 
         else:
@@ -246,7 +253,6 @@ def eval(self):
                 self.dino.eval()
             else:
                 self.dino.decoder.eval()
-            self.dino.encoder.pos_embed.requires_grad = False
         else:
             self.classifier.eval()
 
@@ -259,7 +265,7 @@ def train(self):
             else:
                 self.dino.decoder.train()
             # Finetune learnable pos_embedding
-            self.dino.encoder.pos_embed.requires_grad = self.kwargs["tune_pos_embed"]
+
         else:
             self.classifier.train()
 
@@ -389,7 +395,7 @@ def main(
     log_path: Path = "runs",
     epochs: int = 120,
     flip_augmentations: bool = False,
-    tune_pos_embed: bool = False,
+    pos_embed: str = "pre-trained",
 ):
     torch.set_float32_matmul_precision("high")
     torch.backends.cudnn.benchmark = True
@@ -424,7 +430,7 @@ def main(
         alpha=alpha,
         lift=lift,
         flip_augmentations=flip_augmentations,
-        tune_pos_embed=tune_pos_embed,
+        pos_embed=pos_embed,
     )
 
     writer = get_tensorboard_logger(logging_dir)
@@ -439,7 +445,7 @@ def main(
         use_lora=use_lora,
         alpha=alpha,
         rank=rank,
-        tune_pos_embed=tune_pos_embed,
+        pos_embed=pos_embed,
     )
 
     # noinspection PyArgumentList
@@ -518,7 +524,7 @@ def main(
             "rank": rank,
             "alpha": alpha,
             "dropout_p": dropout_p,
-            "tune_pos_embed": tune_pos_embed,
+            "pos_embed": pos_embed,
         },
     )
 
@@ -636,7 +642,6 @@ def train_step(
         enumerate(train_dataloader), desc="Training", total=len(train_dataloader)
     ):
         global_step += 1
-
         data, labels = prepare_data_f(data, labels)
         smoothed_label = smoothing_fn(labels)
         data = augmentation_fn(data)
@@ -895,9 +900,11 @@ def get_argparser():
     )
 
     parser.add_argument(
-        "--tune_pos_embed",
-        action="store_true",
-        help="Whether to fine-tune the positional embedding if applicable",
+        "--pos_embed",
+        type=str,
+        default="pre-trained",
+        choices=["pre-trained", "fine-tune", "zeros", "zeros-fine-tune"],
+        help="How to handle positional embeddings",
     )
 
     parser.add_argument("--rank", type=int, default=16, help="rank of LoRA")