@@ -1871,5 +1871,132 @@ def _inject_pairs(self, root, num_pairs, same):
18711871 datasets_utils .create_image_folder (root , name2 , lambda _ : f"{ name2 } { no2 :04d} , 1 , 250 )
18721872
18731873
1874+ class SintelTestCase (datasets_utils .ImageDatasetTestCase ):
1875+ DATASET_CLASS = datasets .Sintel
1876+ ADDITIONAL_CONFIGS = datasets_utils .combinations_grid (split = ("train" , "test" ), pass_name = ("clean" , "final" ))
1877+ # We patch the flow reader, because this would otherwise force us to generate fake (but readable) .flo files,
1878+ # which is something we want to # avoid.
1879+ _FAKE_FLOW = "Fake Flow"
1880+ EXTRA_PATCHES = {unittest .mock .patch ("torchvision.datasets.Sintel._read_flow" , return_value = _FAKE_FLOW )}
1881+ FEATURE_TYPES = (PIL .Image .Image , PIL .Image .Image , (type (_FAKE_FLOW ), type (None )))
1882+
1883+ def inject_fake_data (self , tmpdir , config ):
1884+ root = pathlib .Path (tmpdir ) / "Sintel"
1885+
1886+ num_images_per_scene = 3 if config ["split" ] == "train" else 4
1887+ num_scenes = 2
1888+
1889+ for split_dir in ("training" , "test" ):
1890+ for pass_name in ("clean" , "final" ):
1891+ image_root = root / split_dir / pass_name
1892+
1893+ for scene_id in range (num_scenes ):
1894+ scene_dir = image_root / f"scene_{ scene_id }
1895+ datasets_utils .create_image_folder (
1896+ image_root ,
1897+ name = str (scene_dir ),
1898+ file_name_fn = lambda image_idx : f"frame_000{ image_idx } ,
1899+ num_examples = num_images_per_scene ,
1900+ )
1901+
1902+ # For the ground truth flow value we just create empty files so that they're properly discovered,
1903+ # see comment above about EXTRA_PATCHES
1904+ flow_root = root / "training" / "flow"
1905+ for scene_id in range (num_scenes ):
1906+ scene_dir = flow_root / f"scene_{ scene_id }
1907+ os .makedirs (scene_dir )
1908+ for i in range (num_images_per_scene - 1 ):
1909+ open (str (scene_dir / f"frame_000{ i } ), "a" ).close ()
1910+
1911+ # with e.g. num_images_per_scene = 3, for a single scene with have 3 images
1912+ # which are frame_0000, frame_0001 and frame_0002
1913+ # They will be consecutively paired as (frame_0000, frame_0001), (frame_0001, frame_0002),
1914+ # that is 3 - 1 = 2 examples. Hence the formula below
1915+ num_examples = (num_images_per_scene - 1 ) * num_scenes
1916+ return num_examples
1917+
1918+ def test_flow (self ):
1919+ # Make sure flow exists for train split, and make sure there are as many flow values as (pairs of) images
1920+ with self .create_dataset (split = "train" ) as (dataset , _ ):
1921+ assert dataset ._flow_list and len (dataset ._flow_list ) == len (dataset ._image_list )
1922+ for _ , _ , flow in dataset :
1923+ assert flow == self ._FAKE_FLOW
1924+
1925+ # Make sure flow is always None for test split
1926+ with self .create_dataset (split = "test" ) as (dataset , _ ):
1927+ assert dataset ._image_list and not dataset ._flow_list
1928+ for _ , _ , flow in dataset :
1929+ assert flow is None
1930+
1931+ def test_bad_input (self ):
1932+ with pytest .raises (ValueError , match = "split must be either" ):
1933+ with self .create_dataset (split = "bad" ):
1934+ pass
1935+
1936+ with pytest .raises (ValueError , match = "pass_name must be either" ):
1937+ with self .create_dataset (pass_name = "bad" ):
1938+ pass
1939+
1940+
1941+ class KittiFlowTestCase (datasets_utils .ImageDatasetTestCase ):
1942+ DATASET_CLASS = datasets .KittiFlow
1943+ ADDITIONAL_CONFIGS = datasets_utils .combinations_grid (split = ("train" , "test" ))
1944+ FEATURE_TYPES = (PIL .Image .Image , PIL .Image .Image , (np .ndarray , type (None )), (np .ndarray , type (None )))
1945+
1946+ def inject_fake_data (self , tmpdir , config ):
1947+ root = pathlib .Path (tmpdir ) / "Kitti"
1948+
1949+ num_examples = 2 if config ["split" ] == "train" else 3
1950+ for split_dir in ("training" , "testing" ):
1951+
1952+ datasets_utils .create_image_folder (
1953+ root / split_dir ,
1954+ name = "image_2" ,
1955+ file_name_fn = lambda image_idx : f"{ image_idx } ,
1956+ num_examples = num_examples ,
1957+ )
1958+ datasets_utils .create_image_folder (
1959+ root / split_dir ,
1960+ name = "image_2" ,
1961+ file_name_fn = lambda image_idx : f"{ image_idx } ,
1962+ num_examples = num_examples ,
1963+ )
1964+
1965+ # For kitti the ground truth flows are encoded as 16-bits pngs.
1966+ # create_image_folder() will actually create 8-bits pngs, but it doesn't
1967+ # matter much: the flow reader will still be able to read the files, it
1968+ # will just be garbage flow value - but we don't care about that here.
1969+ datasets_utils .create_image_folder (
1970+ root / "training" ,
1971+ name = "flow_occ" ,
1972+ file_name_fn = lambda image_idx : f"{ image_idx } ,
1973+ num_examples = num_examples ,
1974+ )
1975+
1976+ return num_examples
1977+
1978+ def test_flow_and_valid (self ):
1979+ # Make sure flow exists for train split, and make sure there are as many flow values as (pairs of) images
1980+ # Also assert flow and valid are of the expected shape
1981+ with self .create_dataset (split = "train" ) as (dataset , _ ):
1982+ assert dataset ._flow_list and len (dataset ._flow_list ) == len (dataset ._image_list )
1983+ for _ , _ , flow , valid in dataset :
1984+ two , h , w = flow .shape
1985+ assert two == 2
1986+ assert valid .shape == (h , w )
1987+
1988+ # Make sure flow and valid are always None for test split
1989+ with self .create_dataset (split = "test" ) as (dataset , _ ):
1990+ assert dataset ._image_list and not dataset ._flow_list
1991+ for _ , _ , flow , valid in dataset :
1992+ assert flow is None
1993+ assert valid is None
1994+
1995+ def test_bad_input (self ):
1996+ with pytest .raises (ValueError , match = "split must be either" ):
1997+ with self .create_dataset (split = "bad" ):
1998+ pass
1999+
2000+
18742001if __name__ == "__main__" :
18752002 unittest .main ()
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