From f0a6e38ccd03e2594bdc27d59e708655b90e1eb4 Mon Sep 17 00:00:00 2001
From: manonBlanco <blanco@teklia.com>
Date: Mon, 17 Jul 2023 09:16:29 +0200
Subject: [PATCH] Always use 1D and 2D positional embedding
---
dan/decoder.py | 12 ++----------
dan/ocr/document/train.py | 2 --
docs/get_started/training.md | 1 -
docs/usage/train/parameters.md | 2 --
tests/conftest.py | 2 --
tests/data/prediction/parameters.yml | 1 -
6 files changed, 2 insertions(+), 18 deletions(-)
diff --git a/dan/decoder.py b/dan/decoder.py
index af5c28cb..69e372cf 100644
--- a/dan/decoder.py
+++ b/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)
diff --git a/dan/ocr/document/train.py b/dan/ocr/document/train.py
index 6f5e17b3..ad4643b9 100644
--- a/dan/ocr/document/train.py
+++ b/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": {
diff --git a/docs/get_started/training.md b/docs/get_started/training.md
index bc0764c3..15106d40 100644
--- a/docs/get_started/training.md
+++ b/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
diff --git a/docs/usage/train/parameters.md b/docs/usage/train/parameters.md
index bc346d86..ac4026fe 100644
--- a/docs/usage/train/parameters.md
+++ b/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` |
diff --git a/tests/conftest.py b/tests/conftest.py
index 136cead1..e804cb36 100644
--- a/tests/conftest.py
+++ b/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": {
diff --git a/tests/data/prediction/parameters.yml b/tests/data/prediction/parameters.yml
index 469afc8d..2bad8803 100644
--- a/tests/data/prediction/parameters.yml
+++ b/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
--
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