Added InStereo2k dataset

docs/source/datasets.rst

@@ -111,6 +111,7 @@ Stereo Matching
     CarlaStereo
     Kitti2012Stereo
     Kitti2015Stereo
+    InStereo2k
 
 Image pairs
 ~~~~~~~~~~~

test/test_datasets.py

@@ -13,7 +13,7 @@
 import unittest
 import xml.etree.ElementTree as ET
 import zipfile
-from typing import Union
+from typing import Tuple, Union
 
 import datasets_utils
 import numpy as np
@@ -2841,5 +2841,46 @@ def test_train_splits(self):
                 datasets_utils.shape_test_for_stereo(left, right, disparity)
 
 
+class InStereo2k(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.InStereo2k
+    FEATURE_TYPES = (PIL.Image.Image, PIL.Image.Image, (np.ndarray, type(None)))
+    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(split=("train", "test"))
+
+    @staticmethod
+    def _make_scene_folder(root: str, name: str, size: Tuple[int, int]):
+        root = pathlib.Path(root) / name
+        os.makedirs(root, exist_ok=True)
+
+        datasets_utils.create_image_file(root=root, name="left.png", size=(3, size[0], size[1]))
+        datasets_utils.create_image_file(root=root, name="right.png", size=(3, size[0], size[1]))
+        datasets_utils.create_image_file(root=root, name="left_disp.png", size=(1, size[0], size[1]))
+        datasets_utils.create_image_file(root=root, name="right_disp.png", size=(1, size[0], size[1]))
+
+    def inject_fake_data(self, tmpdir, config):
+        in_stereo_dir = pathlib.Path(tmpdir) / "InStereo2k"
+        os.makedirs(in_stereo_dir, exist_ok=True)
+
+        split_dir = pathlib.Path(in_stereo_dir) / config["split"]
+        os.makedirs(split_dir, exist_ok=True)
+
+        num_examples = {"train": 4, "test": 5}.get(config["split"], 0)
+
+        for i in range(num_examples):
+            self._make_scene_folder(split_dir, f"scene_{i:06d}", (100, 200))
+
+        return num_examples
+
+    def test_splits(self):
+        for split_name in ["train", "test"]:
+            with self.create_dataset(split=split_name) as (dataset, _):
+                for left, right, disparity in dataset:
+                    datasets_utils.shape_test_for_stereo(left, right, disparity)
+
+    def test_bad_input(self):
+        with pytest.raises(ValueError, match="Unknown value 'bad' for argument split"):
+            with self.create_dataset(split="bad"):
+                pass
+
+
 if __name__ == "__main__":
     unittest.main()

torchvision/datasets/__init__.py

@@ -1,5 +1,5 @@
 from ._optical_flow import FlyingChairs, FlyingThings3D, HD1K, KittiFlow, Sintel
-from ._stereo_matching import CarlaStereo, Kitti2012Stereo, Kitti2015Stereo
+from ._stereo_matching import CarlaStereo, InStereo2k, Kitti2012Stereo, Kitti2015Stereo
 from .caltech import Caltech101, Caltech256
 from .celeba import CelebA
 from .cifar import CIFAR10, CIFAR100
@@ -109,4 +109,5 @@
     "Kitti2012Stereo",
     "Kitti2015Stereo",
     "CarlaStereo",
+    "InStereo2k",
 )

torchvision/datasets/_stereo_matching.py

@@ -359,3 +359,72 @@ def __getitem__(self, index: int) -> Tuple:
             Both ``disparity`` and ``valid_mask`` are ``None`` if the dataset split is test.
         """
         return super().__getitem__(index)
+
+
+class InStereo2k(StereoMatchingDataset):
+    """`InStereo2k <https://github.com/YuhuaXu/StereoDataset>`_ dataset.
+
+    The dataset is expected to have the following structre: ::
+
+        root
+            InStereo2k
+                train
+                    scene1
+                        left.png
+                        right.png
+                        left_disp.png
+                        right_disp.png
+                        ...
+                    scene2
+                    ...
+                test
+                    scene1
+                        left.png
+                        right.png
+                        left_disp.png
+                        right_disp.png
+                        ...
+                    scene2
+                    ...
+
+    Args:
+        root (string): Root directory where InStereo2k is located.
+        split (string): Either "train" or "test".
+        transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version.
+    """
+
+    def __init__(self, root: str, split: str = "train", transforms: Optional[Callable] = None):
+        super().__init__(root, transforms)
+
+        root = Path(root) / "InStereo2k" / split
+
+        verify_str_arg(split, "split", valid_values=("train", "test"))
+
+        left_img_pattern = str(root / "*" / "left.png")
+        right_img_pattern = str(root / "*" / "right.png")
+        self._images = self._scan_pairs(left_img_pattern, right_img_pattern)
+
+        left_disparity_pattern = str(root / "*" / "left_disp.png")
+        right_disparity_pattern = str(root / "*" / "right_disp.png")
+        self._disparities = self._scan_pairs(left_disparity_pattern, right_disparity_pattern)
+
+    def _read_disparity(self, file_path: str) -> Tuple:
+        disparity_map = np.asarray(Image.open(file_path), dtype=np.float32)
+        # unsqueeze disparity to (C, H, W)
+        disparity_map = disparity_map[None, :, :]
+        valid_mask = None
+        return disparity_map, valid_mask
+
+    def __getitem__(self, index: int) -> Tuple:
+        """Return example at given index.
+
+        Args:
+            index(int): The index of the example to retrieve
+
+        Returns:
+            tuple: A 3-tuple with ``(img_left, img_right, disparity)``.
+            The disparity is a numpy array of shape (1, H, W) and the images are PIL images.
+            If a ``valid_mask`` is generated within the ``transforms`` parameter,
+            a 4-tuple with ``(img_left, img_right, disparity, valid_mask)`` is returned.
+        """
+        return super().__getitem__(index)