Add CarlaStereo, Kitti2012Stereo, and Kitti2015Stereo datasets for Stereo(#6269)

test/datasets_utils.py

@@ -16,13 +16,16 @@
 from collections import defaultdict
 from typing import Any, Callable, Dict, Iterator, List, Optional, Sequence, Tuple, Union
 
+import numpy as np
+
 import PIL
 import PIL.Image
 import pytest
 import torch
 import torchvision.datasets
 import torchvision.io
 from common_utils import disable_console_output, get_tmp_dir
+from torchvision.transforms.functional import get_dimensions
 
 
 __all__ = [
@@ -748,6 +751,49 @@ def size(idx: int) -> Tuple[int, int, int]:
     ]
 
 
+def shape_test_for_stereo_gt_w_mask(
+    left: PIL.Image.Image, right: PIL.Image.Image, disparity: np.ndarray, valid_mask: np.ndarray
+):
+    left_dims = get_dimensions(left)
+    right_dims = get_dimensions(right)
+    c, h, w = left_dims
+    # check that left and right are the same size
+    assert left_dims == right_dims
+    # check general shapes
+    assert c == 3
+    assert disparity.ndim == 3
+    assert disparity.shape == (1, h, w)
+    # check that valid mask is the same size as the disparity
+
+    _, dh, dw = disparity.shape
+    mh, mw = valid_mask.shape
+    assert dh == mh
+    assert dw == mw
+
+
+def shape_test_for_stereo_gt_no_mask(left: PIL.Image.Image, right: PIL.Image.Image, disparity: np.ndarray):
+    left_dims = get_dimensions(left)
+    right_dims = get_dimensions(right)
+    c, h, w = left_dims
+    # check that left and right are the same size
+    assert left_dims == right_dims
+    # check general shapes
+    assert c == 3
+    assert disparity.ndim == 3
+    assert disparity.shape == (1, h, w)
+
+
+def shape_test_for_stereo_no_gt(left: PIL.Image.Image, right: PIL.Image.Image, disparity: None):
+    left_dims = get_dimensions(left)
+    right_dims = get_dimensions(right)
+    c, _, _ = left_dims
+    # check that left and right are the same size
+    assert left_dims == right_dims
+    # check general shapes
+    assert c == 3
+    assert disparity is None
+
+
 @requires_lazy_imports("av")
 def create_video_file(
     root: Union[pathlib.Path, str],

test/test_datasets.py

@@ -13,6 +13,7 @@
 import unittest
 import xml.etree.ElementTree as ET
 import zipfile
+from typing import Union
 
 import datasets_utils
 import numpy as np
@@ -2671,5 +2672,172 @@ def inject_fake_data(self, tmpdir: str, config):
         return len(sampled_classes) * num_images_per_class[config["split"]]
 
 
+class Kitti2012StereoTestCase(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.Kitti2012Stereo
+    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(split=("train", "test"))
+    FEATURE_TYPES = (PIL.Image.Image, PIL.Image.Image, (np.ndarray, type(None)), (np.ndarray, type(None)))
+
+    def inject_fake_data(self, tmpdir, config):
+        kitti_dir = pathlib.Path(tmpdir) / "Kitti2012"
+        os.makedirs(kitti_dir, exist_ok=True)
+
+        split_dir = kitti_dir / (config["split"] + "ing")
+        os.makedirs(split_dir, exist_ok=True)
+
+        num_examples = {"train": 4, "test": 3}.get(config["split"], 0)
+
+        datasets_utils.create_image_folder(
+            root=split_dir,
+            name="colored_0",
+            file_name_fn=lambda i: f"{i:06d}_10.png",
+            num_examples=num_examples,
+            size=(3, 100, 200),
+        )
+        datasets_utils.create_image_folder(
+            root=split_dir,
+            name="colored_1",
+            file_name_fn=lambda i: f"{i:06d}_10.png",
+            num_examples=num_examples,
+            size=(3, 100, 200),
+        )
+
+        if config["split"] == "train":
+            datasets_utils.create_image_folder(
+                root=split_dir,
+                name="disp_noc",
+                file_name_fn=lambda i: f"{i:06d}.png",
+                num_examples=num_examples,
+                # Kitti2012 uses a single channel image for disparities
+                size=(1, 100, 200),
+            )
+
+        return num_examples
+
+    def test_train_splits(self):
+        for split in ["train"]:
+            with self.create_dataset(split=split) as (dataset, _):
+                for left, right, disparity, mask in dataset:
+                    assert mask is None
+                    datasets_utils.shape_test_for_stereo_gt_no_mask(left, right, disparity)
+
+    def test_test_split(self):
+        for split in ["test"]:
+            with self.create_dataset(split=split) as (dataset, _):
+                for left, right, disparity, mask in dataset:
+                    assert mask is None
+                    datasets_utils.shape_test_for_stereo_no_gt(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
+
+
+class Kitti2015StereoTestCase(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.Kitti2015Stereo
+    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(split=("train", "test"))
+    FEATURE_TYPES = (PIL.Image.Image, PIL.Image.Image, (np.ndarray, type(None)), (np.ndarray, type(None)))
+
+    def inject_fake_data(self, tmpdir, config):
+        kitti_dir = pathlib.Path(tmpdir) / "Kitti2015"
+        os.makedirs(kitti_dir, exist_ok=True)
+
+        split_dir = kitti_dir / (config["split"] + "ing")
+        os.makedirs(split_dir, exist_ok=True)
+
+        num_examples = {"train": 4, "test": 6}.get(config["split"], 0)
+
+        datasets_utils.create_image_folder(
+            root=split_dir,
+            name="image_2",
+            file_name_fn=lambda i: f"{i:06d}_10.png",
+            num_examples=num_examples,
+            size=(3, 100, 200),
+        )
+        datasets_utils.create_image_folder(
+            root=split_dir,
+            name="image_3",
+            file_name_fn=lambda i: f"{i:06d}_10.png",
+            num_examples=num_examples,
+            size=(3, 100, 200),
+        )
+
+        if config["split"] == "train":
+            datasets_utils.create_image_folder(
+                root=split_dir,
+                name="disp_occ_0",
+                file_name_fn=lambda i: f"{i:06d}.png",
+                num_examples=num_examples,
+                # Kitti2015 uses a single channel image for disparities
+                size=(1, 100, 200),
+            )
+
+            datasets_utils.create_image_folder(
+                root=split_dir,
+                name="disp_occ_1",
+                file_name_fn=lambda i: f"{i:06d}.png",
+                num_examples=num_examples,
+                # Kitti2015 uses a single channel image for disparities
+                size=(1, 100, 200),
+            )
+
+        return num_examples
+
+    def test_train_splits(self):
+        for split in ["train"]:
+            with self.create_dataset(split=split) as (dataset, _):
+                for left, right, disparity, mask in dataset:
+                    assert mask is None
+                    datasets_utils.shape_test_for_stereo_gt_no_mask(left, right, disparity)
+
+    def test_test_split(self):
+        for split in ["test"]:
+            with self.create_dataset(split=split) as (dataset, _):
+                for left, right, disparity, mask in dataset:
+                    assert mask is None
+                    datasets_utils.shape_test_for_stereo_no_gt(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
+
+
+class CarlaStereoTestCase(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.CarlaStereo
+    FEATURE_TYPES = (PIL.Image.Image, PIL.Image.Image, (np.ndarray, None))
+
+    @staticmethod
+    def _create_scene_folders(num_examples: int, root_dir: Union[str, pathlib.Path]):
+        # make the root_dir if it does not exits
+        os.makedirs(root_dir, exist_ok=True)
+
+        for i in range(num_examples):
+            scene_dir = pathlib.Path(root_dir) / f"scene_{i}"
+            os.makedirs(scene_dir, exist_ok=True)
+            # populate with left right images
+            datasets_utils.create_image_file(root=scene_dir, name="im0.png", size=(100, 100))
+            datasets_utils.create_image_file(root=scene_dir, name="im1.png", size=(100, 100))
+            datasets_utils.make_fake_pfm_file(100, 100, file_name=str(scene_dir / "disp0GT.pfm"))
+            datasets_utils.make_fake_pfm_file(100, 100, file_name=str(scene_dir / "disp1GT.pfm"))
+
+    def inject_fake_data(self, tmpdir, config):
+        carla_dir = pathlib.Path(tmpdir) / "carla-highres"
+        os.makedirs(carla_dir, exist_ok=True)
+
+        split_dir = pathlib.Path(carla_dir) / "trainingF"
+        os.makedirs(split_dir, exist_ok=True)
+
+        num_examples = 6
+        self._create_scene_folders(num_examples=num_examples, root_dir=split_dir)
+
+        return num_examples
+
+    def test_train_splits(self):
+        with self.create_dataset() as (dataset, _):
+            for left, right, disparity in dataset:
+                datasets_utils.shape_test_for_stereo_gt_no_mask(left, right, disparity)
+
+
 if __name__ == "__main__":
     unittest.main()

torchvision/datasets/__init__.py

@@ -1,4 +1,5 @@
 from ._optical_flow import FlyingChairs, FlyingThings3D, HD1K, KittiFlow, Sintel
+from ._stereo_matching import CarlaStereo, Kitti2012Stereo, Kitti2015Stereo
 from .caltech import Caltech101, Caltech256
 from .celeba import CelebA
 from .cifar import CIFAR10, CIFAR100
@@ -105,4 +106,7 @@
     "FGVCAircraft",
     "EuroSAT",
     "RenderedSST2",
+    "Kitti2012Stereo",
+    "Kitti2015Stereo",
+    "CarlaStereo",
 )