Always use 1D and 2D positional embedding

dan/decoder.py

@@ -305,14 +305,9 @@ class FeaturesUpdater(Module):
         self.pe_2d = PositionalEncoding2D(
             params["enc_dim"], params["h_max"], params["w_max"], params["device"]
         )
-        self.use_2d_positional_encoding = (
-            "use_2d_pe" not in params or params["use_2d_pe"]
-        )
 
     def get_pos_features(self, features):
-        if self.use_2d_positional_encoding:
-            return self.pe_2d(features)
-        return features
+        return self.pe_2d(features)
 
 
 class GlobalHTADecoder(Module):
@@ -326,7 +321,6 @@ class GlobalHTADecoder(Module):
         self.dec_att_win = (
             params["attention_win"] if params["attention_win"] is not None else 1
         )
-        self.use_1d_pe = "use_1d_pe" not in params or params["use_1d_pe"]
 
         self.features_updater = FeaturesUpdater(params)
         self.att_decoder = GlobalAttDecoder(params)
@@ -361,9 +355,7 @@ class GlobalHTADecoder(Module):
         pos_tokens = self.emb(tokens).permute(0, 2, 1)
 
         # Add 1D Positional Encoding
-        if self.use_1d_pe:
-            pos_tokens = self.pe_1d(pos_tokens, start=start)
-        pos_tokens = pos_tokens.permute(2, 0, 1)
+        pos_tokens = self.pe_1d(pos_tokens, start=start).permute(2, 0, 1)
 
         if num_pred is None:
             num_pred = tokens.size(1)

dan/ocr/document/train.py

@@ -151,8 +151,6 @@ def get_config():
             "dec_pred_dropout": 0.1,  # dropout rate before decision layer
             "dec_att_dropout": 0.1,  # dropout rate in multi head attention
             "dec_dim_feedforward": 256,  # number of dimension for feedforward layer in transformer decoder layers
-            "use_2d_pe": True,  # use 2D positional embedding
-            "use_1d_pe": True,  # use 1D positional embedding
             "attention_win": 100,  # length of attention window
             # Curriculum dropout
             "dropout_scheduler": {

docs/get_started/training.md

@@ -58,7 +58,6 @@ parameters:
     l_max: int
     dec_pred_dropout: float
     attention_win: int
-    use_1d_pe: bool
     vocab_size: int
     h_max: int
     w_max: int

docs/usage/train/parameters.md

@@ -136,8 +136,6 @@ For a detailed description of all augmentation transforms, see the [dedicated pa
 | `model_params.dec_pred_dropout`           | Dropout rate before decision layer.                                                  | `float`       | `0.1`                                                             |
 | `model_params.dec_att_dropout`            | Dropout rate in multi head attention.                                                | `float`       | `0.1`                                                             |
 | `model_params.dec_dim_feedforward`        | Number of dimensions for feedforward layer in transformer decoder layers.            | `int`         | `256`                                                             |
-| `model_params.use_2d_pe`                  | Whether to use 2D positional embedding.                                              | `bool`        | `True`                                                            |
-| `model_params.use_1d_pe`                  | Whether to use 1D positional embedding.                                              | `bool`        | `True`                                                            |
 | `model_params.attention_win`              | Length of attention window.                                                          | `int`         | `100`                                                             |
 | `model_params.dropout_scheduler.function` | Curriculum dropout scheduler.                                                        | custom class  | `exponential_dropout_scheduler`                                   |
 | `model_params.dropout_scheduler.T`        | Exponential factor.                                                                  | `float`       | `5e4`                                                             |

tests/conftest.py

@@ -99,8 +99,6 @@ def training_config():
             "dec_pred_dropout": 0.1,  # dropout rate before decision layer
             "dec_att_dropout": 0.1,  # dropout rate in multi head attention
             "dec_dim_feedforward": 256,  # number of dimension for feedforward layer in transformer decoder layers
-            "use_2d_pe": True,  # use 2D positional embedding
-            "use_1d_pe": True,  # use 1D positional embedding
             "attention_win": 100,  # length of attention window
             # Curriculum dropout
             "dropout_scheduler": {

tests/data/prediction/parameters.yml

@@ -10,7 +10,6 @@ parameters:
     l_max: 15000
     dec_pred_dropout: 0.1
     attention_win: 100
-    use_1d_pe: True
     vocab_size: 96
     h_max: 500
     w_max: 1000