move tests dir

bio_volumentations/tests/augmentations/test_transforms.py → tests/augmentations/test_transforms.py

 import unittest
 import numpy as np
-from ...augmentations.transforms import *
-from ...core.composition import Compose
+from bio_volumentations.augmentations.transforms import *
+from bio_volumentations.core.composition import Compose
 
 
 class TestResize(unittest.TestCase):

bio_volumentations/tests/conversion/test_transforms.py → tests/conversion/test_transforms.py

 import unittest
 import numpy as np
-import torch
-from volumentations.conversion.transforms import *
+from bio_volumentations.conversion.transforms import *
 
 
 class TestNoConversion(unittest.TestCase):
@@ -34,67 +33,5 @@ class TestNoConversion(unittest.TestCase):
         self.assertListEqual(res_label.tolist(), lbl.tolist())
 
 
-class TestToTensor(unittest.TestCase):
-    def test_image3d(self):
-        sh = (36, 200, 250)
-        img = np.empty(sh)
-        tr = ToTensor()
-        res_image = tr(force_apply=True, targets=[['image']], image=img)['image']
-        self.assertIsInstance(res_image, torch.Tensor)
-        self.assertTupleEqual(tuple(res_image.shape), sh)
-        self.assertListEqual(res_image.tolist(), img.tolist())
-
-    def test_image4d(self):
-        sh = (36, 200, 250)
-        img = np.empty(sh + (4,))
-        tr = ToTensor()
-        res_image = tr(force_apply=True, targets=[['image']], image=img)['image']
-        self.assertIsInstance(res_image, torch.Tensor)
-        self.assertTupleEqual(tuple(res_image.shape), (4,) + sh)
-        self.assertListEqual(res_image.tolist(), img.transpose((3, 0, 1, 2)).tolist())
-
-    def test_image_labels_transpose(self):
-        sh = (36, 200, 250)
-        img = np.empty(sh + (2,), dtype=float)
-        lbl = np.empty(sh, dtype=float)
-        tr = ToTensor()
-        result = tr(force_apply=True, targets=[['image'], ['mask']], image=img, mask=lbl)
-        res_image = result['image']
-        res_label = result['mask']
-        self.assertIsInstance(res_image, torch.Tensor)
-        self.assertTupleEqual(tuple(res_image.shape), (2,) + sh)
-        self.assertListEqual(res_image.tolist(), img.transpose((3, 0, 1, 2)).tolist())
-        self.assertIsInstance(res_label, torch.Tensor)
-        self.assertTupleEqual(tuple(res_label.shape), sh)
-        self.assertListEqual(res_label.tolist(), lbl.tolist())
-
-        lbl = np.empty(sh + (3,), dtype=float)
-        tr = ToTensor()
-        result = tr(force_apply=True, targets=[['image'], ['mask']], image=img, mask=lbl)
-        res_image = result['image']
-        res_label = result['mask']
-        self.assertIsInstance(res_image, torch.Tensor)
-        self.assertTupleEqual(tuple(res_image.shape), (2,) + sh)
-        self.assertListEqual(res_image.tolist(), img.transpose((3, 0, 1, 2)).tolist())
-        self.assertIsInstance(res_label, torch.Tensor)
-        self.assertTupleEqual(tuple(res_label.shape), (3,) + sh)
-        self.assertListEqual(res_label.tolist(), lbl.transpose((3, 0, 1, 2)).tolist())
-
-    def test_image_labels_no_transpose(self):
-        sh = (36, 200, 250)
-        img = np.empty(sh + (2,), dtype=float)
-        lbl = np.empty(sh + (3,), dtype=float)
-        tr = ToTensor(transpose_mask=False)
-        result = tr(force_apply=True, targets=[['image'], ['mask']], image=img, mask=lbl)
-        res_image = result['image']
-        res_label = result['mask']
-        self.assertIsInstance(res_image, torch.Tensor)
-        self.assertTupleEqual(tuple(res_image.shape), (2,) + sh)
-        self.assertListEqual(res_image.tolist(), img.transpose((3, 0, 1, 2)).tolist())
-        self.assertIsInstance(res_label, torch.Tensor)
-        self.assertTupleEqual(tuple(res_label.shape), sh + (3,))
-        self.assertListEqual(res_label.tolist(), lbl.tolist())
-
-
 if __name__ == '__main__':
     unittest.main()