add tests for CelebA

test/test_datasets.py

@@ -639,5 +639,122 @@ class CIFAR100(CIFAR10TestCase):
     )
 
 
+class CelebATestCase(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.CelebA
+    FEATURE_TYPES = (PIL.Image.Image, (torch.Tensor, int, tuple, type(None)))
+
+    CONFIGS = datasets_utils.combinations_grid(
+        split=("train", "valid", "test", "all"),
+        target_type=("attr", "identity", "bbox", "landmarks", ["attr", "identity"]),
+    )
+    REQUIRED_PACKAGES = ("pandas",)
+
+    _SPLIT_TO_IDX = dict(train=0, valid=1, test=2)
+
+    def inject_fake_data(self, tmpdir, config):
+        base_folder = pathlib.Path(tmpdir) / "celeba"
+        os.makedirs(base_folder)
+
+        num_images, num_images_per_split = self._create_split_txt(base_folder)
+
+        datasets_utils.create_image_folder(
+            base_folder, "img_align_celeba", lambda idx: f"{idx + 1:06d}.jpg", num_images
+        )
+        attr_names = self._create_attr_txt(base_folder, num_images)
+        self._create_identity_txt(base_folder, num_images)
+        self._create_bbox_txt(base_folder, num_images)
+        self._create_landmarks_txt(base_folder, num_images)
+
+        return dict(num_examples=num_images_per_split[config["split"]], attr_names=attr_names)
+
+    def _create_split_txt(self, root):
+        num_images_per_split = dict(train=3, valid=2, test=1)
+
+        data = [
+            [self._SPLIT_TO_IDX[split]] for split, num_images in num_images_per_split.items() for _ in range(num_images)
+        ]
+        self._create_txt(root, "list_eval_partition.txt", data)
+
+        num_images_per_split["all"] = num_images = sum(num_images_per_split.values())
+        return num_images, num_images_per_split
+
+    def _create_attr_txt(self, root, num_images):
+        header = ("5_o_Clock_Shadow", "Young")
+        data = torch.rand((num_images, len(header))).ge(0.5).int().mul(2).sub(1).tolist()
+        self._create_txt(root, "list_attr_celeba.txt", data, header=header, add_num_examples=True)
+        return header
+
+    def _create_identity_txt(self, root, num_images):
+        data = torch.randint(1, 4, size=(num_images, 1)).tolist()
+        self._create_txt(root, "identity_CelebA.txt", data)
+
+    def _create_bbox_txt(self, root, num_images):
+        header = ("x_1", "y_1", "width", "height")
+        data = torch.randint(10, size=(num_images, len(header))).tolist()
+        self._create_txt(
+            root, "list_bbox_celeba.txt", data, header=header, add_num_examples=True, add_image_id_to_header=True
+        )
+
+    def _create_landmarks_txt(self, root, num_images):
+        header = ("lefteye_x", "rightmouth_y")
+        data = torch.randint(10, size=(num_images, len(header))).tolist()
+        self._create_txt(root, "list_landmarks_align_celeba.txt", data, header=header, add_num_examples=True)
+
+    def _create_txt(self, root, name, data, header=None, add_num_examples=False, add_image_id_to_header=False):
+        with open(pathlib.Path(root) / name, "w") as fh:
+            if add_num_examples:
+                fh.write(f"{len(data)}\n")
+
+            if header:
+                if add_image_id_to_header:
+                    header = ("image_id", *header)
+                fh.write(f"{' '.join(header)}\n")
+
+            for idx, line in enumerate(data, 1):
+                fh.write(f"{' '.join((f'{idx:06d}.jpg', *[str(value) for value in line]))}\n")
+
+    def test_combined_targets(self):
+        target_types = ["attr", "identity", "bbox", "landmarks"]
+
+        individual_targets = []
+        for target_type in target_types:
+            with self.create_dataset(target_type=target_type) as (dataset, _):
+                _, target = dataset[0]
+                individual_targets.append(target)
+
+        with self.create_dataset(target_type=target_types) as (dataset, _):
+            _, combined_targets = dataset[0]
+
+        actual = len(individual_targets)
+        expected = len(combined_targets)
+        self.assertEqual(
+            actual,
+            expected,
+            f"The number of the returned combined targets does not match the the number targets if requested "
+            f"individually: {actual} != {expected}",
+        )
+
+        for target_type, combined_target, individual_target in zip(target_types, combined_targets, individual_targets):
+            with self.subTest(target_type=target_type):
+                actual = type(combined_target)
+                expected = type(individual_target)
+                self.assertIs(
+                    actual,
+                    expected,
+                    f"Type of the combined target does not match the type of the corresponding individual target: "
+                    f"{actual} is not {expected}",
+                )
+
+    def test_no_target(self):
+        with self.create_dataset(target_type=[]) as (dataset, _):
+            _, target = dataset[0]
+
+        self.assertIsNone(target)
+
+    def test_attr_names(self):
+        with self.create_dataset() as (dataset, info):
+            self.assertEqual(tuple(dataset.attr_names), info["attr_names"])
+
+
 if __name__ == "__main__":
     unittest.main()