1 files changed, 10 insertions, 10 deletions
diff --git a/text_recognizer/data/iam_paragraphs.py b/text_recognizer/data/iam_paragraphs.py
index 24409bc..6022804 100644
--- a/text_recognizer/data/iam_paragraphs.py
+++ b/text_recognizer/data/iam_paragraphs.py
@@ -6,7 +6,7 @@ from typing import Dict, List, Optional, Sequence, Tuple
 from loguru import logger
 import numpy as np
 from PIL import Image, ImageOps
-import torchvision.transforms as transforms
+import torchvision.transforms as T
 from torchvision.transforms.functional import InterpolationMode
 from tqdm import tqdm
 
@@ -270,31 +270,31 @@ def _load_processed_crops_and_labels(
     return ordered_crops, ordered_labels
 
 
-def get_transform(image_shape: Tuple[int, int], augment: bool) -> transforms.Compose:
+def get_transform(image_shape: Tuple[int, int], augment: bool) -> T.Compose:
     """Get transformations for images."""
     if augment:
         transforms_list = [
-            transforms.RandomCrop(
+            T.RandomCrop(
                 size=image_shape,
                 padding=None,
                 pad_if_needed=True,
                 fill=0,
                 padding_mode="constant",
             ),
-            transforms.ColorJitter(brightness=(0.8, 1.6)),
-            transforms.RandomAffine(
+            T.ColorJitter(brightness=(0.8, 1.6)),
+            T.RandomAffine(
                 degrees=1, shear=(-10, 10), interpolation=InterpolationMode.BILINEAR,
             ),
         ]
     else:
-        transforms_list = [transforms.CenterCrop(image_shape)]
-    transforms_list.append(transforms.ToTensor())
-    return transforms.Compose(transforms_list)
+        transforms_list = [T.CenterCrop(image_shape)]
+    transforms_list.append(T.ToTensor())
+    return T.Compose(transforms_list)
 
 
-def get_target_transform(word_pieces: bool) -> Optional[transforms.Compose]:
+def get_target_transform(word_pieces: bool) -> Optional[T.Compose]:
     """Transform emnist characters to word pieces."""
-    return transforms.Compose([WordPiece()]) if word_pieces else None
+    return T.Compose([WordPiece()]) if word_pieces else None
 
 
 def _labels_filename(split: str) -> Path: