Fixed typing and typos, train script load config, reformatted

10 files changed, 23 insertions, 64 deletions

diff --git a/text_recognizer/data/iam_extended_paragraphs.py b/text_recognizer/data/iam_extended_paragraphs.py
index c144341..d2529b4 100644
--- a/text_recognizer/data/iam_extended_paragraphs.py
+++ b/text_recognizer/data/iam_extended_paragraphs.py
@@ -18,16 +18,10 @@ class IAMExtendedParagraphs(BaseDataModule):
         super().__init__(batch_size, num_workers)
 
         self.iam_paragraphs = IAMParagraphs(
-            batch_size,
-            num_workers,
-            train_fraction,
-            augment,
+            batch_size, num_workers, train_fraction, augment,
         )
         self.iam_synthetic_paragraphs = IAMSyntheticParagraphs(
-            batch_size,
-            num_workers,
-            train_fraction,
-            augment,
+            batch_size, num_workers, train_fraction, augment,
         )
 
         self.dims = self.iam_paragraphs.dims
diff --git a/text_recognizer/data/iam_paragraphs.py b/text_recognizer/data/iam_paragraphs.py
index 314d458..f588587 100644
--- a/text_recognizer/data/iam_paragraphs.py
+++ b/text_recognizer/data/iam_paragraphs.py
@@ -161,10 +161,7 @@ def get_dataset_properties() -> Dict:
             "min": min(_get_property_values("num_lines")),
             "max": max(_get_property_values("num_lines")),
         },
-        "crop_shape": {
-            "min": crop_shapes.min(axis=0),
-            "max": crop_shapes.max(axis=0),
-        },
+        "crop_shape": {"min": crop_shapes.min(axis=0), "max": crop_shapes.max(axis=0),},
         "aspect_ratio": {
             "min": aspect_ratio.min(axis=0),
             "max": aspect_ratio.max(axis=0),
@@ -285,9 +282,7 @@ def get_transform(image_shape: Tuple[int, int], augment: bool) -> transforms.Com
             ),
             transforms.ColorJitter(brightness=(0.8, 1.6)),
             transforms.RandomAffine(
-                degrees=1,
-                shear=(-10, 10),
-                interpolation=InterpolationMode.BILINEAR,
+                degrees=1, shear=(-10, 10), interpolation=InterpolationMode.BILINEAR,
             ),
         ]
     else:
diff --git a/text_recognizer/models/base.py b/text_recognizer/models/base.py
index f4df3f3..3c1919e 100644
--- a/text_recognizer/models/base.py
+++ b/text_recognizer/models/base.py
@@ -2,7 +2,7 @@
 from typing import Any, Dict, List, Union, Tuple, Type
 
 import madgrad
-from omegaconf import OmegaConf
+from omegaconf import DictConfig, OmegaConf
 import pytorch_lightning as pl
 import torch
 from torch import nn
@@ -16,9 +16,9 @@ class LitBaseModel(pl.LightningModule):
     def __init__(
         self,
         network: Type[nn.Module],
-        optimizer: Union[OmegaConf, Dict],
-        lr_scheduler: Union[OmegaConf, Dict],
-        criterion: Union[OmegaConf, Dict],
+        optimizer: Union[DictConfig, Dict],
+        lr_scheduler: Union[DictConfig, Dict],
+        criterion: Union[DictConfig, Dict],
         monitor: str = "val_loss",
     ) -> None:
         super().__init__()
@@ -34,7 +34,7 @@ class LitBaseModel(pl.LightningModule):
         self.test_acc = torchmetrics.Accuracy()
 
     @staticmethod
-    def configure_criterion(criterion: Union[OmegaConf, Dict]) -> Type[nn.Module]:
+    def configure_criterion(criterion: Union[DictConfig, Dict]) -> Type[nn.Module]:
         """Returns a loss functions."""
         criterion = OmegaConf.create(criterion)
         args = {} or criterion.args
diff --git a/text_recognizer/models/transformer.py b/text_recognizer/models/transformer.py
index 983e274..b23685b 100644
--- a/text_recognizer/models/transformer.py
+++ b/text_recognizer/models/transformer.py
@@ -1,7 +1,7 @@
 """PyTorch Lightning model for base Transformers."""
 from typing import Dict, List, Optional, Union, Tuple
 
-from omegaconf import OmegaConf
+from omegaconf import DictConfig, OmegaConf
 import pytorch_lightning as pl
 import torch
 from torch import nn
@@ -20,9 +20,9 @@ class LitTransformerModel(LitBaseModel):
     def __init__(
         self,
         network: Type[nn, Module],
-        optimizer: Union[OmegaConf, Dict],
-        lr_scheduler: Union[OmegaConf, Dict],
-        criterion: Union[OmegaConf, Dict],
+        optimizer: Union[DictConfig, Dict],
+        lr_scheduler: Union[DictConfig, Dict],
+        criterion: Union[DictConfig, Dict],
         monitor: str = "val_loss",
         mapping: Optional[List[str]] = None,
     ) -> None:
diff --git a/text_recognizer/networks/__init__.py b/text_recognizer/networks/__init__.py
index 4dcaf2e..979149f 100644
--- a/text_recognizer/networks/__init__.py
+++ b/text_recognizer/networks/__init__.py
@@ -1,3 +1,2 @@
 """Network modules"""
 from .image_transformer import ImageTransformer
-
diff --git a/text_recognizer/networks/image_transformer.py b/text_recognizer/networks/image_transformer.py
index edebca9..9ed67a4 100644
--- a/text_recognizer/networks/image_transformer.py
+++ b/text_recognizer/networks/image_transformer.py
@@ -13,7 +13,7 @@ import math
 from typing import Dict, List, Union, Sequence, Tuple, Type
 
 from einops import rearrange
-from omegaconf import OmegaConf
+from omegaconf import DictConfig, OmegaConf
 import torch
 from torch import nn
 from torch import Tensor
@@ -34,7 +34,7 @@ class ImageTransformer(nn.Module):
         self,
         input_shape: Sequence[int],
         output_shape: Sequence[int],
-        encoder: Union[OmegaConf, Dict],
+        encoder: Union[DictConfig, Dict],
         mapping: str,
         num_decoder_layers: int = 4,
         hidden_dim: int = 256,
@@ -101,7 +101,7 @@ class ImageTransformer(nn.Module):
             nn.init.normal_(self.feature_map_encoding.bias, -bound, bound)
 
     @staticmethod
-    def _configure_encoder(encoder: Union[OmegaConf, NamedTuple]) -> Type[nn.Module]:
+    def _configure_encoder(encoder: Union[DictConfig, Dict]) -> Type[nn.Module]:
         encoder = OmegaConf.create(encoder)
         network_module = importlib.import_module("text_recognizer.networks")
         encoder_class = getattr(network_module, encoder.type)
diff --git a/text_recognizer/networks/residual_network.py b/text_recognizer/networks/residual_network.py
index da7553d..c33f419 100644
--- a/text_recognizer/networks/residual_network.py
+++ b/text_recognizer/networks/residual_network.py
@@ -20,11 +20,7 @@ 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 b10f93a..d7e3d08 100644
--- a/text_recognizer/networks/transducer/transducer.py
+++ b/text_recognizer/networks/transducer/transducer.py
@@ -392,12 +392,7 @@ 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 67ed0d9..8847aba 100644
--- a/text_recognizer/networks/vqvae/decoder.py
+++ b/text_recognizer/networks/vqvae/decoder.py
@@ -44,12 +44,7 @@ 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(
@@ -78,9 +73,7 @@ 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)
@@ -109,12 +102,7 @@ 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 ede5c31..d3adac5 100644
--- a/text_recognizer/networks/vqvae/encoder.py
+++ b/text_recognizer/networks/vqvae/encoder.py
@@ -11,10 +11,7 @@ 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 = [
@@ -138,12 +135,7 @@ 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)