Linting of iam paragraphs and preprocessor

1 files changed, 12 insertions, 7 deletions

diff --git a/text_recognizer/data/iam_paragraphs.py b/text_recognizer/data/iam_paragraphs.py
index 262533f..74b6165 100644
--- a/text_recognizer/data/iam_paragraphs.py
+++ b/text_recognizer/data/iam_paragraphs.py
@@ -11,12 +11,12 @@ import torchvision.transforms as T
 from torchvision.transforms.functional import InterpolationMode
 from tqdm import tqdm
 
+from text_recognizer.data.base_data_module import BaseDataModule, load_and_print_info
 from text_recognizer.data.base_dataset import (
     BaseDataset,
     convert_strings_to_labels,
     split_dataset,
 )
-from text_recognizer.data.base_data_module import BaseDataModule, load_and_print_info
 from text_recognizer.data.emnist_mapping import EmnistMapping
 from text_recognizer.data.iam import IAM
 from text_recognizer.data.transforms import WordPiece
@@ -55,7 +55,7 @@ class IAMParagraphs(BaseDataModule):
 
         log.info("Cropping IAM paragraph regions and saving them along with labels...")
 
-        iam = IAM(mapping=EmnistMapping(extra_symbols={NEW_LINE_TOKEN,}))
+        iam = IAM(mapping=EmnistMapping(extra_symbols={NEW_LINE_TOKEN}))
         iam.prepare_data()
 
         properties = {}
@@ -134,10 +134,14 @@ class IAMParagraphs(BaseDataModule):
             f"{len(self.data_train)}, "
             f"{len(self.data_val)}, "
             f"{len(self.data_test)}\n"
-            f"Train Batch x stats: {(x.shape, x.dtype, x.min(), x.mean(), x.std(), x.max())}\n"
-            f"Train Batch y stats: {(y.shape, y.dtype, y.min(), y.max())}\n"
-            f"Test Batch x stats: {(xt.shape, xt.dtype, xt.min(), xt.mean(), xt.std(), xt.max())}\n"
-            f"Test Batch y stats: {(yt.shape, yt.dtype, yt.min(), yt.max())}\n"
+            "Train Batch x stats: "
+            f"{(x.shape, x.dtype, x.min(), x.mean(), x.std(), x.max())}\n"
+            "Train Batch y stats: "
+            f"{(y.shape, y.dtype, y.min(), y.max())}\n"
+            "Test Batch x stats: "
+            f"{(xt.shape, xt.dtype, xt.min(), xt.mean(), xt.std(), xt.max())}\n"
+            "Test Batch y stats: "
+            f"{(yt.shape, yt.dtype, yt.min(), yt.max())}\n"
         )
         return basic + data
 
@@ -161,7 +165,7 @@ 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),
@@ -316,4 +320,5 @@ def _num_lines(label: str) -> int:
 
 
 def create_iam_paragraphs() -> None:
+    """Loads and displays dataset statistics."""
     load_and_print_info(IAMParagraphs)