Port test/test_utils.py to pytest (#3917)

Author: zhiqwang

test/test_utils.py

@@ -5,7 +5,7 @@
 import tempfile
 import torch
 import torchvision.utils as utils
-import unittest
+
 from io import BytesIO
 import torchvision.transforms.functional as F
 from PIL import Image, __version__ as PILLOW_VERSION, ImageColor
@@ -18,122 +18,131 @@
                      [10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float)
 
 
-class Tester(unittest.TestCase):
-
-    def test_make_grid_not_inplace(self):
-        t = torch.rand(5, 3, 10, 10)
-        t_clone = t.clone()
-
-        utils.make_grid(t, normalize=False)
-        assert_equal(t, t_clone, msg='make_grid modified tensor in-place')
-
-        utils.make_grid(t, normalize=True, scale_each=False)
-        assert_equal(t, t_clone, msg='make_grid modified tensor in-place')
-
-        utils.make_grid(t, normalize=True, scale_each=True)
-        assert_equal(t, t_clone, msg='make_grid modified tensor in-place')
-
-    def test_normalize_in_make_grid(self):
-        t = torch.rand(5, 3, 10, 10) * 255
-        norm_max = torch.tensor(1.0)
-        norm_min = torch.tensor(0.0)
-
-        grid = utils.make_grid(t, normalize=True)
-        grid_max = torch.max(grid)
-        grid_min = torch.min(grid)
-
-        # Rounding the result to one decimal for comparison
-        n_digits = 1
-        rounded_grid_max = torch.round(grid_max * 10 ** n_digits) / (10 ** n_digits)
-        rounded_grid_min = torch.round(grid_min * 10 ** n_digits) / (10 ** n_digits)
-
-        assert_equal(norm_max, rounded_grid_max, msg='Normalized max is not equal to 1')
-        assert_equal(norm_min, rounded_grid_min, msg='Normalized min is not equal to 0')
-
-    @unittest.skipIf(sys.platform in ('win32', 'cygwin'), 'temporarily disabled on Windows')
-    def test_save_image(self):
-        with tempfile.NamedTemporaryFile(suffix='.png') as f:
-            t = torch.rand(2, 3, 64, 64)
-            utils.save_image(t, f.name)
-            self.assertTrue(os.path.exists(f.name), 'The image is not present after save')
-
-    @unittest.skipIf(sys.platform in ('win32', 'cygwin'), 'temporarily disabled on Windows')
-    def test_save_image_single_pixel(self):
-        with tempfile.NamedTemporaryFile(suffix='.png') as f:
-            t = torch.rand(1, 3, 1, 1)
-            utils.save_image(t, f.name)
-            self.assertTrue(os.path.exists(f.name), 'The pixel image is not present after save')
-
-    @unittest.skipIf(sys.platform in ('win32', 'cygwin'), 'temporarily disabled on Windows')
-    def test_save_image_file_object(self):
-        with tempfile.NamedTemporaryFile(suffix='.png') as f:
-            t = torch.rand(2, 3, 64, 64)
-            utils.save_image(t, f.name)
-            img_orig = Image.open(f.name)
-            fp = BytesIO()
-            utils.save_image(t, fp, format='png')
-            img_bytes = Image.open(fp)
-            assert_equal(F.to_tensor(img_orig), F.to_tensor(img_bytes), msg='Image not stored in file object')
-
-    @unittest.skipIf(sys.platform in ('win32', 'cygwin'), 'temporarily disabled on Windows')
-    def test_save_image_single_pixel_file_object(self):
-        with tempfile.NamedTemporaryFile(suffix='.png') as f:
-            t = torch.rand(1, 3, 1, 1)
-            utils.save_image(t, f.name)
-            img_orig = Image.open(f.name)
-            fp = BytesIO()
-            utils.save_image(t, fp, format='png')
-            img_bytes = Image.open(fp)
-            assert_equal(F.to_tensor(img_orig), F.to_tensor(img_bytes), msg='Image not stored in file object')
-
-    def test_draw_boxes(self):
-        img = torch.full((3, 100, 100), 255, dtype=torch.uint8)
-        img_cp = img.clone()
-        boxes_cp = boxes.clone()
-        labels = ["a", "b", "c", "d"]
-        colors = ["green", "#FF00FF", (0, 255, 0), "red"]
-        result = utils.draw_bounding_boxes(img, boxes, labels=labels, colors=colors, fill=True)
-
-        path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_boxes_util.png")
-        if not os.path.exists(path):
-            res = Image.fromarray(result.permute(1, 2, 0).contiguous().numpy())
-            res.save(path)
-
-        if PILLOW_VERSION >= (8, 2):
-            # The reference image is only valid for new PIL versions
-            expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
-            assert_equal(result, expected)
-
-        # Check if modification is not in place
-        assert_equal(boxes, boxes_cp)
-        assert_equal(img, img_cp)
-
-    def test_draw_boxes_vanilla(self):
-        img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
-        img_cp = img.clone()
-        boxes_cp = boxes.clone()
-        result = utils.draw_bounding_boxes(img, boxes, fill=False, width=7)
-
-        path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_boxes_vanilla.png")
-        if not os.path.exists(path):
-            res = Image.fromarray(result.permute(1, 2, 0).contiguous().numpy())
-            res.save(path)
+def test_make_grid_not_inplace():
+    t = torch.rand(5, 3, 10, 10)
+    t_clone = t.clone()
+
+    utils.make_grid(t, normalize=False)
+    assert_equal(t, t_clone, msg='make_grid modified tensor in-place')
+
+    utils.make_grid(t, normalize=True, scale_each=False)
+    assert_equal(t, t_clone, msg='make_grid modified tensor in-place')
+
+    utils.make_grid(t, normalize=True, scale_each=True)
+    assert_equal(t, t_clone, msg='make_grid modified tensor in-place')
+
+
+def test_normalize_in_make_grid():
+    t = torch.rand(5, 3, 10, 10) * 255
+    norm_max = torch.tensor(1.0)
+    norm_min = torch.tensor(0.0)
+
+    grid = utils.make_grid(t, normalize=True)
+    grid_max = torch.max(grid)
+    grid_min = torch.min(grid)
+
+    # Rounding the result to one decimal for comparison
+    n_digits = 1
+    rounded_grid_max = torch.round(grid_max * 10 ** n_digits) / (10 ** n_digits)
+    rounded_grid_min = torch.round(grid_min * 10 ** n_digits) / (10 ** n_digits)
+
+    assert_equal(norm_max, rounded_grid_max, msg='Normalized max is not equal to 1')
+    assert_equal(norm_min, rounded_grid_min, msg='Normalized min is not equal to 0')
+
+
+@pytest.mark.skipif(sys.platform in ('win32', 'cygwin'), reason='temporarily disabled on Windows')
+def test_save_image():
+    with tempfile.NamedTemporaryFile(suffix='.png') as f:
+        t = torch.rand(2, 3, 64, 64)
+        utils.save_image(t, f.name)
+        assert os.path.exists(f.name), 'The image is not present after save'
 
+
+@pytest.mark.skipif(sys.platform in ('win32', 'cygwin'), reason='temporarily disabled on Windows')
+def test_save_image_single_pixel():
+    with tempfile.NamedTemporaryFile(suffix='.png') as f:
+        t = torch.rand(1, 3, 1, 1)
+        utils.save_image(t, f.name)
+        assert os.path.exists(f.name), 'The pixel image is not present after save'
+
+
+@pytest.mark.skipif(sys.platform in ('win32', 'cygwin'), reason='temporarily disabled on Windows')
+def test_save_image_file_object():
+    with tempfile.NamedTemporaryFile(suffix='.png') as f:
+        t = torch.rand(2, 3, 64, 64)
+        utils.save_image(t, f.name)
+        img_orig = Image.open(f.name)
+        fp = BytesIO()
+        utils.save_image(t, fp, format='png')
+        img_bytes = Image.open(fp)
+        assert_equal(F.to_tensor(img_orig), F.to_tensor(img_bytes), msg='Image not stored in file object')
+
+
+@pytest.mark.skipif(sys.platform in ('win32', 'cygwin'), reason='temporarily disabled on Windows')
+def test_save_image_single_pixel_file_object():
+    with tempfile.NamedTemporaryFile(suffix='.png') as f:
+        t = torch.rand(1, 3, 1, 1)
+        utils.save_image(t, f.name)
+        img_orig = Image.open(f.name)
+        fp = BytesIO()
+        utils.save_image(t, fp, format='png')
+        img_bytes = Image.open(fp)
+        assert_equal(F.to_tensor(img_orig), F.to_tensor(img_bytes), msg='Image not stored in file object')
+
+
+def test_draw_boxes():
+    img = torch.full((3, 100, 100), 255, dtype=torch.uint8)
+    img_cp = img.clone()
+    boxes_cp = boxes.clone()
+    labels = ["a", "b", "c", "d"]
+    colors = ["green", "#FF00FF", (0, 255, 0), "red"]
+    result = utils.draw_bounding_boxes(img, boxes, labels=labels, colors=colors, fill=True)
+
+    path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_boxes_util.png")
+    if not os.path.exists(path):
+        res = Image.fromarray(result.permute(1, 2, 0).contiguous().numpy())
+        res.save(path)
+
+    if PILLOW_VERSION >= (8, 2):
+        # The reference image is only valid for new PIL versions
         expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
         assert_equal(result, expected)
-        # Check if modification is not in place
-        assert_equal(boxes, boxes_cp)
-        assert_equal(img, img_cp)
 
-    def test_draw_invalid_boxes(self):
-        img_tp = ((1, 1, 1), (1, 2, 3))
-        img_wrong1 = torch.full((3, 5, 5), 255, dtype=torch.float)
-        img_wrong2 = torch.full((1, 3, 5, 5), 255, dtype=torch.uint8)
-        boxes = torch.tensor([[0, 0, 20, 20], [0, 0, 0, 0],
-                             [10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float)
-        self.assertRaises(TypeError, utils.draw_bounding_boxes, img_tp, boxes)
-        self.assertRaises(ValueError, utils.draw_bounding_boxes, img_wrong1, boxes)
-        self.assertRaises(ValueError, utils.draw_bounding_boxes, img_wrong2, boxes)
+    # Check if modification is not in place
+    assert_equal(boxes, boxes_cp)
+    assert_equal(img, img_cp)
+
+
+def test_draw_boxes_vanilla():
+    img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
+    img_cp = img.clone()
+    boxes_cp = boxes.clone()
+    result = utils.draw_bounding_boxes(img, boxes, fill=False, width=7)
+
+    path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_boxes_vanilla.png")
+    if not os.path.exists(path):
+        res = Image.fromarray(result.permute(1, 2, 0).contiguous().numpy())
+        res.save(path)
+
+    expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
+    assert_equal(result, expected)
+    # Check if modification is not in place
+    assert_equal(boxes, boxes_cp)
+    assert_equal(img, img_cp)
+
+
+def test_draw_invalid_boxes():
+    img_tp = ((1, 1, 1), (1, 2, 3))
+    img_wrong1 = torch.full((3, 5, 5), 255, dtype=torch.float)
+    img_wrong2 = torch.full((1, 3, 5, 5), 255, dtype=torch.uint8)
+    boxes = torch.tensor([[0, 0, 20, 20], [0, 0, 0, 0],
+                          [10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float)
+    with pytest.raises(TypeError, match="Tensor expected"):
+        utils.draw_bounding_boxes(img_tp, boxes)
+    with pytest.raises(ValueError, match="Tensor uint8 expected"):
+        utils.draw_bounding_boxes(img_wrong1, boxes)
+    with pytest.raises(ValueError, match="Pass individual images, not batches"):
+        utils.draw_bounding_boxes(img_wrong2, boxes)
 
 
 @pytest.mark.parametrize('colors', [
@@ -218,5 +227,5 @@ def test_draw_segmentation_masks_errors():
         utils.draw_segmentation_masks(image=img, masks=masks, colors=bad_colors)
 
 
-if __name__ == '__main__':
-    unittest.main()
+if __name__ == "__main__":
+    pytest.main([__file__])