From dedf8deb025ac9efdad5e9baf9165ef63d6829ff Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Mon, 5 Apr 2021 23:24:20 +0200
Subject: Pre-commit fixes, optimizer loading fix

---
 text_recognizer/networks/image_transformer.py     | 10 ++++------
 text_recognizer/networks/residual_network.py      |  6 +++++-
 text_recognizer/networks/transducer/transducer.py |  7 ++++++-
 text_recognizer/networks/vqvae/decoder.py         | 18 +++++++++++++++---
 text_recognizer/networks/vqvae/encoder.py         | 12 ++++++++++--
 5 files changed, 40 insertions(+), 13 deletions(-)

(limited to 'text_recognizer/networks')

diff --git a/text_recognizer/networks/image_transformer.py b/text_recognizer/networks/image_transformer.py
index aa024e0..85a84d2 100644
--- a/text_recognizer/networks/image_transformer.py
+++ b/text_recognizer/networks/image_transformer.py
@@ -1,9 +1,9 @@
 """A Transformer with a cnn backbone.
 
 The network encodes a image with a convolutional backbone to a latent representation,
-i.e. feature maps. A 2d positional encoding is applied to the feature maps for 
+i.e. feature maps. A 2d positional encoding is applied to the feature maps for
 spatial information. The resulting feature are then set to a transformer decoder
-together with the target tokens. 
+together with the target tokens.
 
 TODO: Local attention for transformer.j
 
@@ -107,9 +107,7 @@ class ImageTransformer(nn.Module):
         encoder_class = getattr(network_module, encoder.type)
         return encoder_class(**encoder.args)
 
-    def _configure_mapping(
-        self, mapping: str
-    ) -> Tuple[List[str], Dict[str, int]]:
+    def _configure_mapping(self, mapping: str) -> Tuple[List[str], Dict[str, int]]:
         """Configures mapping."""
         if mapping == "emnist":
             mapping, inverse_mapping, _ = emnist_mapping()
@@ -125,7 +123,7 @@ class ImageTransformer(nn.Module):
             Tensor: Image features.
 
         Shapes:
-            - image: :math: `(B, C, H, W)` 
+            - image: :math: `(B, C, H, W)`
             - latent: :math: `(B, T, C)`
 
         """
diff --git a/text_recognizer/networks/residual_network.py b/text_recognizer/networks/residual_network.py
index c33f419..da7553d 100644
--- a/text_recognizer/networks/residual_network.py
+++ b/text_recognizer/networks/residual_network.py
@@ -20,7 +20,11 @@ class Conv2dAuto(nn.Conv2d):
 
 def conv_bn(in_channels: int, out_channels: int, *args, **kwargs) -> nn.Sequential:
     """3x3 convolution with batch norm."""
-    conv3x3 = partial(Conv2dAuto, kernel_size=3, bias=False,)
+    conv3x3 = partial(
+        Conv2dAuto,
+        kernel_size=3,
+        bias=False,
+    )
     return nn.Sequential(
         conv3x3(in_channels, out_channels, *args, **kwargs),
         nn.BatchNorm2d(out_channels),
diff --git a/text_recognizer/networks/transducer/transducer.py b/text_recognizer/networks/transducer/transducer.py
index d7e3d08..b10f93a 100644
--- a/text_recognizer/networks/transducer/transducer.py
+++ b/text_recognizer/networks/transducer/transducer.py
@@ -392,7 +392,12 @@ def load_transducer_loss(
         transitions = gtn.load(str(processed_path / transitions))
 
     preprocessor = Preprocessor(
-        data_dir, num_features, tokens_path, lexicon_path, use_words, prepend_wordsep,
+        data_dir,
+        num_features,
+        tokens_path,
+        lexicon_path,
+        use_words,
+        prepend_wordsep,
     )
 
     num_tokens = preprocessor.num_tokens
diff --git a/text_recognizer/networks/vqvae/decoder.py b/text_recognizer/networks/vqvae/decoder.py
index 8847aba..67ed0d9 100644
--- a/text_recognizer/networks/vqvae/decoder.py
+++ b/text_recognizer/networks/vqvae/decoder.py
@@ -44,7 +44,12 @@ class Decoder(nn.Module):
 
         # Configure encoder.
         self.decoder = self._build_decoder(
-            channels, kernel_sizes, strides, num_residual_layers, activation, dropout,
+            channels,
+            kernel_sizes,
+            strides,
+            num_residual_layers,
+            activation,
+            dropout,
         )
 
     def _build_decompression_block(
@@ -73,7 +78,9 @@ class Decoder(nn.Module):
             )
 
             if i < len(self.upsampling):
-                modules.append(nn.Upsample(size=self.upsampling[i]),)
+                modules.append(
+                    nn.Upsample(size=self.upsampling[i]),
+                )
 
             if dropout is not None:
                 modules.append(dropout)
@@ -102,7 +109,12 @@ class Decoder(nn.Module):
     ) -> nn.Sequential:
 
         self.res_block.append(
-            nn.Conv2d(self.embedding_dim, channels[0], kernel_size=1, stride=1,)
+            nn.Conv2d(
+                self.embedding_dim,
+                channels[0],
+                kernel_size=1,
+                stride=1,
+            )
         )
 
         # Bottleneck module.
diff --git a/text_recognizer/networks/vqvae/encoder.py b/text_recognizer/networks/vqvae/encoder.py
index d3adac5..ede5c31 100644
--- a/text_recognizer/networks/vqvae/encoder.py
+++ b/text_recognizer/networks/vqvae/encoder.py
@@ -11,7 +11,10 @@ from text_recognizer.networks.vqvae.vector_quantizer import VectorQuantizer
 
 class _ResidualBlock(nn.Module):
     def __init__(
-        self, in_channels: int, out_channels: int, dropout: Optional[Type[nn.Module]],
+        self,
+        in_channels: int,
+        out_channels: int,
+        dropout: Optional[Type[nn.Module]],
     ) -> None:
         super().__init__()
         self.block = [
@@ -135,7 +138,12 @@ class Encoder(nn.Module):
         )
 
         encoder.append(
-            nn.Conv2d(channels[-1], self.embedding_dim, kernel_size=1, stride=1,)
+            nn.Conv2d(
+                channels[-1],
+                self.embedding_dim,
+                kernel_size=1,
+                stride=1,
+            )
         )
 
         return nn.Sequential(*encoder)
-- 
cgit v1.2.3-70-g09d2