Return labels for FER2013 if possible (#8452)

test/test_datasets.py

@@ -2442,28 +2442,68 @@ class FER2013TestCase(datasets_utils.ImageDatasetTestCase):
         base_folder = os.path.join(tmpdir, "fer2013")
         os.makedirs(base_folder)
 
+        use_icml = config.pop("use_icml", False)
+        use_fer = config.pop("use_fer", False)
+
         num_samples = 5
-        with open(os.path.join(base_folder, f"{config['split']}.csv"), "w", newline="") as file:
-            writer = csv.DictWriter(
-                file,
-                fieldnames=("emotion", "pixels") if config["split"] == "train" else ("pixels",),
-                quoting=csv.QUOTE_NONNUMERIC,
-                quotechar='"',
-            )
-            writer.writeheader()
-            for _ in range(num_samples):
-                row = dict(
-                    pixels=" ".join(
-                        str(pixel) for pixel in datasets_utils.create_image_or_video_tensor((48, 48)).view(-1).tolist()
-                    )
+
+        if use_icml or use_fer:
+            pixels_key, usage_key = (" pixels", " Usage") if use_icml else ("pixels", "Usage")
+            fieldnames = ("emotion", usage_key, pixels_key) if use_icml else ("emotion", pixels_key, usage_key)
+            filename = "icml_face_data.csv" if use_icml else "fer2013.csv"
+            with open(os.path.join(base_folder, filename), "w", newline="") as file:
+                writer = csv.DictWriter(
+                    file,
+                    fieldnames=fieldnames,
+                    quoting=csv.QUOTE_NONNUMERIC,
+                    quotechar='"',
                 )
-                if config["split"] == "train":
-                    row["emotion"] = str(int(torch.randint(0, 7, ())))
+                writer.writeheader()
+                for i in range(num_samples):
+                    row = {
+                        "emotion": str(int(torch.randint(0, 7, ()))),
+                        usage_key: "Training" if i % 2 else "PublicTest",
+                        pixels_key: " ".join(
+                            str(pixel)
+                            for pixel in datasets_utils.create_image_or_video_tensor((48, 48)).view(-1).tolist()
+                        ),
+                    }
+
+                    writer.writerow(row)
+        else:
+            with open(os.path.join(base_folder, f"{config['split']}.csv"), "w", newline="") as file:
+                writer = csv.DictWriter(
+                    file,
+                    fieldnames=("emotion", "pixels") if config["split"] == "train" else ("pixels",),
+                    quoting=csv.QUOTE_NONNUMERIC,
+                    quotechar='"',
+                )
+                writer.writeheader()
+                for _ in range(num_samples):
+                    row = dict(
+                        pixels=" ".join(
+                            str(pixel)
+                            for pixel in datasets_utils.create_image_or_video_tensor((48, 48)).view(-1).tolist()
+                        )
+                    )
+                    if config["split"] == "train":
+                        row["emotion"] = str(int(torch.randint(0, 7, ())))
 
-                writer.writerow(row)
+                    writer.writerow(row)
 
         return num_samples
 
+    def test_icml_file(self):
+        config = {"split": "test"}
+        with self.create_dataset(config=config) as (dataset, _):
+            assert all(s[1] is None for s in dataset)
+
+        for split in ("train", "test"):
+            for d in ({"use_icml": True}, {"use_fer": True}):
+                config = {"split": split, **d}
+                with self.create_dataset(config=config) as (dataset, _):
+                    assert all(s[1] is not None for s in dataset)
+
 
 class GTSRBTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.GTSRB

torchvision/datasets/fer2013.py

@@ -13,9 +13,21 @@ class FER2013(VisionDataset):
     """`FER2013
     <https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge>`_ Dataset.
 
+    .. note::
+        This dataset can return test labels only if ``fer2013.csv`` OR
+        ``icml_face_data.csv`` are present in ``root/fer2013/``. If only
+        ``train.csv`` and ``test.csv`` are present, the test labels are set to
+        ``None``.
+
     Args:
         root (str or ``pathlib.Path``): Root directory of dataset where directory
-            ``root/fer2013`` exists.
+            ``root/fer2013`` exists. This directory may contain either
+            ``fer2013.csv``, ``icml_face_data.csv``, or both ``train.csv`` and
+            ``test.csv``. Precendence is given in that order, i.e. if
+            ``fer2013.csv`` is present then the rest of the files will be
+            ignored. All these (combinations of) files contain the same data and
+            are supported for convenience, but only ``fer2013.csv`` and
+            ``icml_face_data.csv`` are able to return non-None test labels.
         split (string, optional): The dataset split, supports ``"train"`` (default), or ``"test"``.
         transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
             version. E.g, ``transforms.RandomCrop``
@@ -25,6 +37,25 @@ class FER2013(VisionDataset):
     _RESOURCES = {
         "train": ("train.csv", "3f0dfb3d3fd99c811a1299cb947e3131"),
         "test": ("test.csv", "b02c2298636a634e8c2faabbf3ea9a23"),
+        # The fer2013.csv and icml_face_data.csv files contain both train and
+        # tests instances, and unlike test.csv they contain the labels for the
+        # test instances. We give these 2 files precedence over train.csv and
+        # test.csv. And yes, they both contain the same data, but with different
+        # column names (note the spaces) and ordering:
+        # $ head -n 1 fer2013.csv icml_face_data.csv train.csv test.csv
+        # ==> fer2013.csv <==
+        # emotion,pixels,Usage
+        #
+        # ==> icml_face_data.csv <==
+        # emotion, Usage, pixels
+        #
+        # ==> train.csv <==
+        # emotion,pixels
+        #
+        # ==> test.csv <==
+        # pixels
+        "fer": ("fer2013.csv", "f8428a1edbd21e88f42c73edd2a14f95"),
+        "icml": ("icml_face_data.csv", "b114b9e04e6949e5fe8b6a98b3892b1d"),
     }
 
     def __init__(
@@ -34,11 +65,13 @@ class FER2013(VisionDataset):
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
     ) -> None:
-        self._split = verify_str_arg(split, "split", self._RESOURCES.keys())
+        self._split = verify_str_arg(split, "split", ("train", "test"))
         super().__init__(root, transform=transform, target_transform=target_transform)
 
         base_folder = pathlib.Path(self.root) / "fer2013"
-        file_name, md5 = self._RESOURCES[self._split]
+        use_fer_file = (base_folder / self._RESOURCES["fer"][0]).exists()
+        use_icml_file = not use_fer_file and (base_folder / self._RESOURCES["icml"][0]).exists()
+        file_name, md5 = self._RESOURCES["fer" if use_fer_file else "icml" if use_icml_file else self._split]
         data_file = base_folder / file_name
         if not check_integrity(str(data_file), md5=md5):
             raise RuntimeError(
@@ -47,14 +80,26 @@ class FER2013(VisionDataset):
                 f"https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge"
             )
 
+        pixels_key = " pixels" if use_icml_file else "pixels"
+        usage_key = " Usage" if use_icml_file else "Usage"
+
+        def get_img(row):
+            return torch.tensor([int(idx) for idx in row[pixels_key].split()], dtype=torch.uint8).reshape(48, 48)
+
+        def get_label(row):
+            if use_fer_file or use_icml_file or self._split == "train":
+                return int(row["emotion"])
+            else:
+                return None
+
         with open(data_file, "r", newline="") as file:
-            self._samples = [
-                (
-                    torch.tensor([int(idx) for idx in row["pixels"].split()], dtype=torch.uint8).reshape(48, 48),
-                    int(row["emotion"]) if "emotion" in row else None,
-                )
-                for row in csv.DictReader(file)
-            ]
+            rows = (row for row in csv.DictReader(file))
+
+            if use_fer_file or use_icml_file:
+                valid_keys = ("Training",) if self._split == "train" else ("PublicTest", "PrivateTest")
+                rows = (row for row in rows if row[usage_key] in valid_keys)
+
+            self._samples = [(get_img(row), get_label(row)) for row in rows]
 
     def __len__(self) -> int:
         return len(self._samples)