Add FlyingThings3D dataset for optical flow (#4858)

docs/source/datasets.rst

@@ -44,6 +44,7 @@ You can also create your own datasets using the provided :ref:`base classes <bas
     Flickr8k
     Flickr30k
     FlyingChairs
+    FlyingThings3D
     HMDB51
     ImageNet
     INaturalist

test/datasets_utils.py

@@ -204,7 +204,6 @@ class DatasetTestCase(unittest.TestCase):
             ``transforms``, or ``download``.
         - REQUIRED_PACKAGES (Iterable[str]): Additional dependencies to use the dataset. If these packages are not
             available, the tests are skipped.
-        - EXTRA_PATCHES(set): Additional patches to add for each test, to e.g. mock a specific function
 
     Additionally, you need to overwrite the ``inject_fake_data()`` method that provides the data that the tests rely on.
     The fake data should resemble the original data as close as necessary, while containing only few examples. During
@@ -256,8 +255,6 @@ class DatasetTestCase(unittest.TestCase):
     ADDITIONAL_CONFIGS = None
     REQUIRED_PACKAGES = None
 
-    EXTRA_PATCHES = None
-
     # These keyword arguments are checked by test_transforms in case they are available in DATASET_CLASS.
     _TRANSFORM_KWARGS = {
         "transform",
@@ -383,9 +380,6 @@ class DatasetTestCase(unittest.TestCase):
         if patch_checks:
             patchers.update(self._patch_checks())
 
-        if self.EXTRA_PATCHES is not None:
-            patchers.update(self.EXTRA_PATCHES)
-
         with get_tmp_dir() as tmpdir:
             args = self.dataset_args(tmpdir, complete_config)
             info = self._inject_fake_data(tmpdir, complete_config) if inject_fake_data else None
@@ -393,7 +387,7 @@ class DatasetTestCase(unittest.TestCase):
             with self._maybe_apply_patches(patchers), disable_console_output():
                 dataset = self.DATASET_CLASS(*args, **complete_config, **special_kwargs)
 
-                yield dataset, info
+            yield dataset, info
 
     @classmethod
     def setUpClass(cls):
@@ -925,6 +919,14 @@ def create_random_string(length: int, *digits: str) -> str:
     return "".join(random.choice(digits) for _ in range(length))
 
 
+def make_fake_pfm_file(h, w, file_name):
+    values = list(range(3 * h * w))
+    # Note: we pack everything in little endian: -1.0, and "<"
+    content = f"PF \n{w} {h} \n-1.0\n".encode() + struct.pack("<" + "f" * len(values), *values)
+    with open(file_name, "wb") as f:
+        f.write(content)
+
+
 def make_fake_flo_file(h, w, file_name):
     """Creates a fake flow file in .flo format."""
     values = list(range(2 * h * w))

test/test_datasets.py

@@ -2048,5 +2048,72 @@ class FlyingChairsTestCase(datasets_utils.ImageDatasetTestCase):
                 np.testing.assert_allclose(flow, np.arange(flow.size).reshape(flow.shape))
 
 
+class FlyingThings3DTestCase(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.FlyingThings3D
+    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(
+        split=("train", "test"), pass_name=("clean", "final", "both"), camera=("left", "right", "both")
+    )
+    FEATURE_TYPES = (PIL.Image.Image, PIL.Image.Image, (np.ndarray, type(None)))
+
+    FLOW_H, FLOW_W = 3, 4
+
+    def inject_fake_data(self, tmpdir, config):
+        root = pathlib.Path(tmpdir) / "FlyingThings3D"
+
+        num_images_per_camera = 3 if config["split"] == "train" else 4
+        passes = ("frames_cleanpass", "frames_finalpass")
+        splits = ("TRAIN", "TEST")
+        letters = ("A", "B", "C")
+        subfolders = ("0000", "0001")
+        cameras = ("left", "right")
+        for pass_name, split, letter, subfolder, camera in itertools.product(
+            passes, splits, letters, subfolders, cameras
+        ):
+            current_folder = root / pass_name / split / letter / subfolder
+            datasets_utils.create_image_folder(
+                current_folder,
+                name=camera,
+                file_name_fn=lambda image_idx: f"00{image_idx}.png",
+                num_examples=num_images_per_camera,
+            )
+
+        directions = ("into_future", "into_past")
+        for split, letter, subfolder, direction, camera in itertools.product(
+            splits, letters, subfolders, directions, cameras
+        ):
+            current_folder = root / "optical_flow" / split / letter / subfolder / direction / camera
+            os.makedirs(str(current_folder), exist_ok=True)
+            for i in range(num_images_per_camera):
+                datasets_utils.make_fake_pfm_file(self.FLOW_H, self.FLOW_W, file_name=str(current_folder / f"{i}.pfm"))
+
+        num_cameras = 2 if config["camera"] == "both" else 1
+        num_passes = 2 if config["pass_name"] == "both" else 1
+        num_examples = (
+            (num_images_per_camera - 1) * num_cameras * len(subfolders) * len(letters) * len(splits) * num_passes
+        )
+        return num_examples
+
+    @datasets_utils.test_all_configs
+    def test_flow(self, config):
+        with self.create_dataset(config=config) as (dataset, _):
+            assert dataset._flow_list and len(dataset._flow_list) == len(dataset._image_list)
+            for _, _, flow in dataset:
+                assert flow.shape == (2, self.FLOW_H, self.FLOW_W)
+                # We don't check the values because the reshaping and flipping makes it hard to figure out
+
+    def test_bad_input(self):
+        with pytest.raises(ValueError, match="Unknown value 'bad' for argument split"):
+            with self.create_dataset(split="bad"):
+                pass
+
+        with pytest.raises(ValueError, match="Unknown value 'bad' for argument pass_name"):
+            with self.create_dataset(pass_name="bad"):
+                pass
+
+        with pytest.raises(ValueError, match="Unknown value 'bad' for argument camera"):
+            with self.create_dataset(camera="bad"):
+                pass
+
+
 if __name__ == "__main__":
     unittest.main()

torchvision/datasets/__init__.py

-from ._optical_flow import KittiFlow, Sintel, FlyingChairs
+from ._optical_flow import KittiFlow, Sintel, FlyingChairs, FlyingThings3D
 from .caltech import Caltech101, Caltech256
 from .celeba import CelebA
 from .cifar import CIFAR10, CIFAR100
@@ -75,4 +75,5 @@ __all__ = (
     "KittiFlow",
     "Sintel",
     "FlyingChairs",
+    "FlyingThings3D",
 )

torchvision/datasets/_optical_flow.py

+import itertools
 import os
+import re
 from abc import ABC, abstractmethod
 from glob import glob
 from pathlib import Path
@@ -15,6 +17,7 @@ from .vision import VisionDataset
 __all__ = (
     "KittiFlow",
     "Sintel",
+    "FlyingThings3D",
     "FlyingChairs",
 )
 
@@ -271,6 +274,94 @@ class FlyingChairs(FlowDataset):
         return _read_flo(file_name)
 
 
+class FlyingThings3D(FlowDataset):
+    """`FlyingThings3D <https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html>`_ dataset for optical flow.
+
+    The dataset is expected to have the following structure: ::
+
+        root
+            FlyingThings3D
+                frames_cleanpass
+                    TEST
+                    TRAIN
+                frames_finalpass
+                    TEST
+                    TRAIN
+                optical_flow
+                    TEST
+                    TRAIN
+
+    Args:
+        root (string): Root directory of the intel FlyingThings3D Dataset.
+        split (string, optional): The dataset split, either "train" (default) or "test"
+        pass_name (string, optional): The pass to use, either "clean" (default) or "final" or "both". See link above for
+            details on the different passes.
+        camera (string, optional): Which camera to return images from. Can be either "left" (default) or "right" or "both".
+        transforms (callable, optional): A function/transform that takes in
+            ``img1, img2, flow, valid`` and returns a transformed version.
+            ``valid`` is expected for consistency with other datasets which
+            return a built-in valid mask, such as :class:`~torchvision.datasets.KittiFlow`.
+    """
+
+    def __init__(self, root, split="train", pass_name="clean", camera="left", transforms=None):
+        super().__init__(root=root, transforms=transforms)
+
+        verify_str_arg(split, "split", valid_values=("train", "test"))
+        split = split.upper()
+
+        verify_str_arg(pass_name, "pass_name", valid_values=("clean", "final", "both"))
+        passes = {
+            "clean": ["frames_cleanpass"],
+            "final": ["frames_finalpass"],
+            "both": ["frames_cleanpass", "frames_finalpass"],
+        }[pass_name]
+
+        verify_str_arg(camera, "camera", valid_values=("left", "right", "both"))
+        cameras = ["left", "right"] if camera == "both" else [camera]
+
+        root = Path(root) / "FlyingThings3D"
+
+        directions = ("into_future", "into_past")
+        for pass_name, camera, direction in itertools.product(passes, cameras, directions):
+            image_dirs = sorted(glob(str(root / pass_name / split / "*/*")))
+            image_dirs = sorted([Path(image_dir) / camera for image_dir in image_dirs])
+
+            flow_dirs = sorted(glob(str(root / "optical_flow" / split / "*/*")))
+            flow_dirs = sorted([Path(flow_dir) / direction / camera for flow_dir in flow_dirs])
+
+            if not image_dirs or not flow_dirs:
+                raise FileNotFoundError(
+                    "Could not find the FlyingThings3D flow images. "
+                    "Please make sure the directory structure is correct."
+                )
+
+            for image_dir, flow_dir in zip(image_dirs, flow_dirs):
+                images = sorted(glob(str(image_dir / "*.png")))
+                flows = sorted(glob(str(flow_dir / "*.pfm")))
+                for i in range(len(flows) - 1):
+                    if direction == "into_future":
+                        self._image_list += [[images[i], images[i + 1]]]
+                        self._flow_list += [flows[i]]
+                    elif direction == "into_past":
+                        self._image_list += [[images[i + 1], images[i]]]
+                        self._flow_list += [flows[i + 1]]
+
+    def __getitem__(self, index):
+        """Return example at given index.
+
+        Args:
+            index(int): The index of the example to retrieve
+
+        Returns:
+            tuple: A 3-tuple with ``(img1, img2, flow)``.
+            The flow is a numpy array of shape (2, H, W) and the images are PIL images.
+        """
+        return super().__getitem__(index)
+
+    def _read_flow(self, file_name):
+        return _read_pfm(file_name)
+
+
 def _read_flo(file_name):
     """Read .flo file in Middlebury format"""
     # Code adapted from:
@@ -295,3 +386,31 @@ def _read_16bits_png_with_flow_and_valid_mask(file_name):
 
     # For consistency with other datasets, we convert to numpy
     return flow.numpy(), valid.numpy()
+
+
+def _read_pfm(file_name):
+    """Read flow in .pfm format"""
+
+    with open(file_name, "rb") as f:
+        header = f.readline().rstrip()
+        if header != b"PF":
+            raise ValueError("Invalid PFM file")
+
+        dim_match = re.match(rb"^(\d+)\s(\d+)\s$", f.readline())
+        if not dim_match:
+            raise Exception("Malformed PFM header.")
+        w, h = (int(dim) for dim in dim_match.groups())
+
+        scale = float(f.readline().rstrip())
+        if scale < 0:  # little-endian
+            endian = "<"
+            scale = -scale
+        else:
+            endian = ">"  # big-endian
+
+        data = np.fromfile(f, dtype=endian + "f")
+
+    data = data.reshape(h, w, 3).transpose(2, 0, 1)
+    data = np.flip(data, axis=1)  # flip on h dimension
+    data = data[:2, :, :]
+    return data.astype(np.float32)