Refactoring

invertransforms/lib.py

@@ -40,7 +40,8 @@ def track(self, img, index=None):
 
         Args:
             img (PIL Image, torch.Tensor, Any): input image.
-            index (optional, int or Any): index associated with the tracked inverse transform; increasing int when not defined
+            index (optional, int or Any): index associated with the tracked inverse transform;
+             increasing int when not defined
 
         Returns: image
         """

tests/test_compose.py

@@ -2,23 +2,21 @@
 from PIL import Image
 
 import invertransforms as T
-import invertransforms.sequence
-import invertransforms.util_functions
 from invertransforms.lib import InvertibleError
 from tests.invertible_test_case import InvertibleTestCase
 
 
 class TestCompose(InvertibleTestCase):
 
     def test_invert_of_invertible_only(self):
-        tf = invertransforms.sequence.Compose(['s'])
+        tf = T.Compose(['s'])
         with self.assertRaises(InvertibleError):
             tf.inverse()
 
     def test_nested_invert(self):
-        tf = invertransforms.sequence.Compose([
-            invertransforms.sequence.Compose([
-                invertransforms.util_functions.ToPILImage(),
+        tf = T.Compose([
+            T.Compose([
+                T.ToPILImage(),
                 T.RandomHorizontalFlip(0),
                 T.RandomHorizontalFlip(1),
             ]),

tests/test_five_crop.py

@@ -1,12 +1,12 @@
-import invertransforms.crop_pad
+import invertransforms as T
 from invertransforms.lib import InvertibleError
 from tests.invertible_test_case import InvertibleTestCase
 
 
 class TestFiveCrop(InvertibleTestCase):
     def setUp(self) -> None:
         super().setUp()
-        self.tf = invertransforms.crop_pad.FiveCrop(size=self.crop_size)
+        self.tf = T.FiveCrop(size=self.crop_size)
 
     def test_call(self):
         outputs = self.tf(self.img_pil)

tests/test_normalize.py

@@ -1,11 +1,11 @@
 import torch
 
-import invertransforms.tensors
+import invertransforms as T
 from tests.invertible_test_case import InvertibleTestCase
 
 
 class TestNormalize(InvertibleTestCase):
 
     def test_invert(self):
-        tf = invertransforms.tensors.Normalize(mean=[0.5], std=[0.5])
+        tf = T.Normalize(mean=[0.5], std=[0.5])
         self.assertTrue(torch.allclose(self.img_tensor, tf.invert(tf(self.img_tensor)), atol=1e-07))

tests/test_random_transforms.py

@@ -3,7 +3,6 @@
 
 import invertransforms as T
 import invertransforms.crop_pad
-import invertransforms.util_functions
 from invertransforms.lib import InvertibleError
 from tests.invertible_test_case import InvertibleTestCase
 
@@ -16,7 +15,7 @@ def test_invert_before_apply(self):
 
     def test_invert(self):
         tf = T.RandomApply(
-            transforms=[T.Identity(), invertransforms.util_functions.ToTensor()],
+            transforms=[T.Identity(), T.ToTensor()],
             p=1,
         )
 
@@ -26,7 +25,7 @@ def test_invert(self):
 
     def test_identity(self):
         tf_id = T.RandomApply(
-            transforms=invertransforms.util_functions.ToPILImage(),
+            transforms=T.ToPILImage(),
             p=0,
         )
 
@@ -42,10 +41,10 @@ def test_invert_before_apply(self):
 
     def test_invert(self):
         tf = T.RandomChoice(
-            [invertransforms.util_functions.ToTensor()] * 10
+            [T.ToTensor()] * 10
         )
         self.assertIsInstance(tf(self.img_pil), torch.Tensor)
-        self.assertIsInstance(tf.inverse(), invertransforms.util_functions.ToPILImage)
+        self.assertIsInstance(tf.inverse(), T.ToPILImage)
 
 
 class TestRandomOrder(InvertibleTestCase):

tests/test_ten_crop.py

@@ -1,12 +1,12 @@
-import invertransforms.crop_pad
+import invertransforms as T
 from invertransforms.lib import InvertibleError
 from tests.invertible_test_case import InvertibleTestCase
 
 
 class TestTenCrop(InvertibleTestCase):
     def setUp(self) -> None:
         super().setUp()
-        self.tf = invertransforms.crop_pad.TenCrop(size=self.crop_size)
+        self.tf = T.TenCrop(size=self.crop_size)
 
     def test_call(self):
         outputs = self.tf(self.img_pil)
@@ -16,7 +16,7 @@ def test_call(self):
             self.assertEqual(img.size[::-1], self.crop_size)  # [::-1] because pil order is inversed
 
     def test_call_vflip(self):
-        tf = invertransforms.crop_pad.TenCrop(self.crop_size, vertical_flip=True)
+        tf = T.TenCrop(self.crop_size, vertical_flip=True)
         outputs = tf(self.img_pil)
         self.assertEqual(len(outputs), 10)
 

tests/test_transform_if.py

@@ -1,5 +1,4 @@
 import invertransforms as T
-import invertransforms.util_functions
 from invertransforms.lib import InvertibleError
 from tests.invertible_test_case import InvertibleTestCase
 
@@ -17,9 +16,9 @@ def test_not_a_transform(self):
             tf.inverse()
 
     def test_invert(self):
-        tf = T.TransformIf(transform=invertransforms.util_functions.ToTensor(), condition=True)
-        self.assertIsInstance(tf.inverse(), invertransforms.util_functions.ToPILImage)
+        tf = T.TransformIf(transform=T.ToTensor(), condition=True)
+        self.assertIsInstance(tf.inverse(), T.ToPILImage)
 
     def test_repr(self):
-        tf = invertransforms.util_functions.ToPILImage()
+        tf = T.ToPILImage()
         self.assertEqual(repr(tf), repr(T.TransformIf(tf, True)))