add tests for LSUN

test/test_datasets.py

@@ -23,6 +23,8 @@
 import shutil
 import json
 import random
+import string
+import io
 
 
 try:
@@ -954,5 +956,85 @@ def _create_annotation_file(self, root, name, video_files):
             fh.writelines(f"{file}\n" for file in sorted(video_files))
 
 
+class LSUNTestCase(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.LSUN
+
+    REQUIRED_PACKAGES = ("lmdb",)
+    CONFIGS = datasets_utils.combinations_grid(
+        classes=("train", "test", "val", ["bedroom_train", "church_outdoor_train"])
+    )
+
+    _CATEGORIES = (
+        "bedroom",
+        "bridge",
+        "church_outdoor",
+        "classroom",
+        "conference_room",
+        "dining_room",
+        "kitchen",
+        "living_room",
+        "restaurant",
+        "tower",
+    )
+
+    def inject_fake_data(self, tmpdir, config):
+        root = pathlib.Path(tmpdir)
+
+        num_images = 0
+        for cls in self._parse_classes(config["classes"]):
+            num_images += self._create_lmdb(root, cls)
+
+        return num_images
+
+    @contextlib.contextmanager
+    def create_dataset(
+        self,
+        *args, **kwargs
+    ):
+        with super().create_dataset(*args, **kwargs) as output:
+            yield output
+            # Currently datasets.LSUN caches the keys in the current directory rather than in the root directory. Thus,
+            # this creates a number of unique _cache_* files in the current directory that will not be removed together
+            # with the temporary directory
+            for file in os.listdir(os.getcwd()):
+                if file.startswith("_cache_"):
+                    os.remove(file)
+
+    def _parse_classes(self, classes):
+        if not isinstance(classes, str):
+            return classes
+
+        split = classes
+        if split == "test":
+            return [split]
+
+        return [f"{category}_{split}" for category in self._CATEGORIES]
+
+    def _create_lmdb(self, root, cls):
+        lmdb = datasets_utils.lazy_importer.lmdb
+        hexdigits_lowercase = string.digits + string.ascii_lowercase[:6]
+
+        folder = f"{cls}_lmdb"
+
+        num_images = torch.randint(1, 4, size=()).item()
+        format = "webp"
+        files = datasets_utils.create_image_folder(root, folder, lambda idx: f"{idx}.{format}", num_images)
+
+        with lmdb.open(str(root / folder)) as env, env.begin(write=True) as txn:
+            for file in files:
+                key = "".join(random.choice(hexdigits_lowercase) for _ in range(40)).encode()
+
+                buffer = io.BytesIO()
+                Image.open(file).save(buffer, format)
+                buffer.seek(0)
+                value = buffer.read()
+
+                txn.put(key, value)
+
+                os.remove(file)
+
+        return num_images
+
+
 if __name__ == "__main__":
     unittest.main()