Added binary cat vs dog classification target type to Oxford pet dataset (#8388)

Co-authored-by: Nicolas Hug <nh.nicolas.hug@gmail.com>
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

test/test_datasets.py

@@ -2553,7 +2553,7 @@ class OxfordIIITPetTestCase(datasets_utils.ImageDatasetTestCase):
 
     ADDITIONAL_CONFIGS = combinations_grid(
         split=("trainval", "test"),
-        target_types=("category", "segmentation", ["category", "segmentation"], []),
+        target_types=("category", "binary-category", "segmentation", ["category", "segmentation"], []),
     )
 
     def inject_fake_data(self, tmpdir, config):

torchvision/datasets/oxford_iiit_pet.py

@@ -19,6 +19,7 @@ class OxfordIIITPet(VisionDataset):
             ``segmentation``. Can also be a list to output a tuple with all specified target types. The types represent:
 
                 - ``category`` (int): Label for one of the 37 pet categories.
+                - ``binary-category`` (int): Binary label for cat or dog.
                 - ``segmentation`` (PIL image): Segmentation trimap of the image.
 
             If empty, ``None`` will be returned as target.
@@ -34,7 +35,7 @@ class OxfordIIITPet(VisionDataset):
         ("https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz", "5c4f3ee8e5d25df40f4fd59a7f44e54c"),
         ("https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz", "95a8c909bbe2e81eed6a22bccdf3f68f"),
     )
-    _VALID_TARGET_TYPES = ("category", "segmentation")
+    _VALID_TARGET_TYPES = ("category", "binary-category", "segmentation")
 
     def __init__(
         self,
@@ -67,12 +68,15 @@ class OxfordIIITPet(VisionDataset):
 
         image_ids = []
         self._labels = []
+        self._bin_labels = []
         with open(self._anns_folder / f"{self._split}.txt") as file:
             for line in file:
-                image_id, label, *_ = line.strip().split()
+                image_id, label, bin_label, _ = line.strip().split()
                 image_ids.append(image_id)
                 self._labels.append(int(label) - 1)
+                self._bin_labels.append(int(bin_label) - 1)
 
+        self.bin_classes = ["Cat", "Dog"]
         self.classes = [
             " ".join(part.title() for part in raw_cls.split("_"))
             for raw_cls, _ in sorted(
@@ -80,6 +84,7 @@ class OxfordIIITPet(VisionDataset):
                 key=lambda image_id_and_label: image_id_and_label[1],
             )
         ]
+        self.bin_class_to_idx = dict(zip(self.bin_classes, range(len(self.bin_classes))))
         self.class_to_idx = dict(zip(self.classes, range(len(self.classes))))
 
         self._images = [self._images_folder / f"{image_id}.jpg" for image_id in image_ids]
@@ -95,6 +100,8 @@ class OxfordIIITPet(VisionDataset):
         for target_type in self._target_types:
             if target_type == "category":
                 target.append(self._labels[idx])
+            elif target_type == "binary-category":
+                target.append(self._bin_labels[idx])
             else:  # target_type == "segmentation"
                 target.append(Image.open(self._segs[idx]))