Port some tests in test_transforms.py to pytest (#3964)

test/test_transforms.py

@@ -403,94 +403,6 @@ class Tester(unittest.TestCase):
         with self.assertRaisesRegex(ValueError, r"Required crop size .+ is larger then input image size .+"):
             t(img)
 
-    @unittest.skipIf(stats is None, 'scipy.stats not available')
-    def test_random_apply(self):
-        random_state = random.getstate()
-        random.seed(42)
-        random_apply_transform = transforms.RandomApply(
-            [
-                transforms.RandomRotation((-45, 45)),
-                transforms.RandomHorizontalFlip(),
-                transforms.RandomVerticalFlip(),
-            ], p=0.75
-        )
-        img = transforms.ToPILImage()(torch.rand(3, 10, 10))
-        num_samples = 250
-        num_applies = 0
-        for _ in range(num_samples):
-            out = random_apply_transform(img)
-            if out != img:
-                num_applies += 1
-
-        p_value = stats.binom_test(num_applies, num_samples, p=0.75)
-        random.setstate(random_state)
-        self.assertGreater(p_value, 0.0001)
-
-        # Checking if RandomApply can be printed as string
-        random_apply_transform.__repr__()
-
-    @unittest.skipIf(stats is None, 'scipy.stats not available')
-    def test_random_choice(self):
-        random_state = random.getstate()
-        random.seed(42)
-        random_choice_transform = transforms.RandomChoice(
-            [
-                transforms.Resize(15),
-                transforms.Resize(20),
-                transforms.CenterCrop(10)
-            ]
-        )
-        img = transforms.ToPILImage()(torch.rand(3, 25, 25))
-        num_samples = 250
-        num_resize_15 = 0
-        num_resize_20 = 0
-        num_crop_10 = 0
-        for _ in range(num_samples):
-            out = random_choice_transform(img)
-            if out.size == (15, 15):
-                num_resize_15 += 1
-            elif out.size == (20, 20):
-                num_resize_20 += 1
-            elif out.size == (10, 10):
-                num_crop_10 += 1
-
-        p_value = stats.binom_test(num_resize_15, num_samples, p=0.33333)
-        self.assertGreater(p_value, 0.0001)
-        p_value = stats.binom_test(num_resize_20, num_samples, p=0.33333)
-        self.assertGreater(p_value, 0.0001)
-        p_value = stats.binom_test(num_crop_10, num_samples, p=0.33333)
-        self.assertGreater(p_value, 0.0001)
-
-        random.setstate(random_state)
-        # Checking if RandomChoice can be printed as string
-        random_choice_transform.__repr__()
-
-    @unittest.skipIf(stats is None, 'scipy.stats not available')
-    def test_random_order(self):
-        random_state = random.getstate()
-        random.seed(42)
-        random_order_transform = transforms.RandomOrder(
-            [
-                transforms.Resize(20),
-                transforms.CenterCrop(10)
-            ]
-        )
-        img = transforms.ToPILImage()(torch.rand(3, 25, 25))
-        num_samples = 250
-        num_normal_order = 0
-        resize_crop_out = transforms.CenterCrop(10)(transforms.Resize(20)(img))
-        for _ in range(num_samples):
-            out = random_order_transform(img)
-            if out == resize_crop_out:
-                num_normal_order += 1
-
-        p_value = stats.binom_test(num_normal_order, num_samples, p=0.5)
-        random.setstate(random_state)
-        self.assertGreater(p_value, 0.0001)
-
-        # Checking if RandomOrder can be printed as string
-        random_order_transform.__repr__()
-
     def test_to_tensor(self):
         test_channels = [1, 3, 4]
         height, width = 4, 4
@@ -1994,5 +1906,96 @@ def test_random_grayscale():
     trans3.__repr__()
 
 
+@pytest.mark.skipif(stats is None, reason='scipy.stats not available')
+def test_random_apply():
+    random_state = random.getstate()
+    random.seed(42)
+    random_apply_transform = transforms.RandomApply(
+        [
+            transforms.RandomRotation((-45, 45)),
+            transforms.RandomHorizontalFlip(),
+            transforms.RandomVerticalFlip(),
+        ], p=0.75
+    )
+    img = transforms.ToPILImage()(torch.rand(3, 10, 10))
+    num_samples = 250
+    num_applies = 0
+    for _ in range(num_samples):
+        out = random_apply_transform(img)
+        if out != img:
+            num_applies += 1
+
+    p_value = stats.binom_test(num_applies, num_samples, p=0.75)
+    random.setstate(random_state)
+    assert p_value > 0.0001
+
+    # Checking if RandomApply can be printed as string
+    random_apply_transform.__repr__()
+
+
+@pytest.mark.skipif(stats is None, reason='scipy.stats not available')
+def test_random_choice():
+    random_state = random.getstate()
+    random.seed(42)
+    random_choice_transform = transforms.RandomChoice(
+        [
+            transforms.Resize(15),
+            transforms.Resize(20),
+            transforms.CenterCrop(10)
+        ]
+    )
+    img = transforms.ToPILImage()(torch.rand(3, 25, 25))
+    num_samples = 250
+    num_resize_15 = 0
+    num_resize_20 = 0
+    num_crop_10 = 0
+    for _ in range(num_samples):
+        out = random_choice_transform(img)
+        if out.size == (15, 15):
+            num_resize_15 += 1
+        elif out.size == (20, 20):
+            num_resize_20 += 1
+        elif out.size == (10, 10):
+            num_crop_10 += 1
+
+    p_value = stats.binom_test(num_resize_15, num_samples, p=0.33333)
+    assert p_value > 0.0001
+    p_value = stats.binom_test(num_resize_20, num_samples, p=0.33333)
+    assert p_value > 0.0001
+    p_value = stats.binom_test(num_crop_10, num_samples, p=0.33333)
+    assert p_value > 0.0001
+
+    random.setstate(random_state)
+    # Checking if RandomChoice can be printed as string
+    random_choice_transform.__repr__()
+
+
+@pytest.mark.skipif(stats is None, reason='scipy.stats not available')
+def test_random_order():
+    random_state = random.getstate()
+    random.seed(42)
+    random_order_transform = transforms.RandomOrder(
+        [
+            transforms.Resize(20),
+            transforms.CenterCrop(10)
+        ]
+    )
+    img = transforms.ToPILImage()(torch.rand(3, 25, 25))
+    num_samples = 250
+    num_normal_order = 0
+    resize_crop_out = transforms.CenterCrop(10)(transforms.Resize(20)(img))
+    for _ in range(num_samples):
+        out = random_order_transform(img)
+        if out == resize_crop_out:
+            num_normal_order += 1
+
+    p_value = stats.binom_test(num_normal_order, num_samples, p=0.5)
+    random.setstate(random_state)
+    assert p_value > 0.0001
+
+    # Checking if RandomOrder can be printed as string
+    random_order_transform.__repr__()
+
+
 if __name__ == '__main__':
     unittest.main()