fix splitting CelebA dataset + corresponding test

test/test_datasets.py

@@ -512,7 +512,7 @@ def inject_fake_data(self, tmpdir, config):
         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)
+        num_images_per_split = dict(train=4, valid=3, test=2)
 
         data = [
             [self._SPLIT_TO_IDX[split]] for split, num_images in num_images_per_split.items() for _ in range(num_images)
@@ -595,6 +595,17 @@ def test_attr_names(self):
         with self.create_dataset() as (dataset, info):
             assert tuple(dataset.attr_names) == info["attr_names"]
 
+    def test_images_names_split(self):
+        with self.create_dataset(split='all') as (dataset, _):
+            all_imgs_names = set(dataset.filename)
+
+        merged_imgs_names = set()
+        for split in ["train", "valid", "test"]:
+            with self.create_dataset(split=split) as (dataset, _):
+                merged_imgs_names.update(dataset.filename)
+
+        assert merged_imgs_names == all_imgs_names
+
 
 class VOCSegmentationTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.VOCSegmentation

torchvision/datasets/celeba.py

@@ -99,7 +99,10 @@ def __init__(
 
         mask = slice(None) if split_ is None else (splits.data == split_).squeeze()
 
-        self.filename = splits.index
+        if mask == slice(None):  # if split == "all"
+            self.filename = splits.index
+        else:
+            self.filename = [splits.index[i] for i in torch.squeeze(torch.nonzero(mask))]
         self.identity = identity.data[mask]
         self.bbox = bbox.data[mask]
         self.landmarks_align = landmarks_align.data[mask]