fix syntax

Author: AntonioCarta

avalanche/benchmarks/classic/__init__.py

@@ -13,4 +13,4 @@
 from .clear import *
 from .stream51 import *
 from .ex_model import *
-from .concon import *
+from .concon import *

avalanche/benchmarks/classic/concon.py

@@ -6,7 +6,9 @@
 from torchvision import transforms
 
 from avalanche.benchmarks.utils.data import AvalancheDataset
-from avalanche.benchmarks.utils.classification_dataset import _as_taskaware_supervised_classification_dataset
+from avalanche.benchmarks.utils.classification_dataset import (
+    _as_taskaware_supervised_classification_dataset,
+)
 from avalanche.benchmarks import benchmark_from_datasets, CLScenario
 
 from avalanche.benchmarks.datasets.concon import ConConDataset
@@ -18,20 +20,14 @@
 _default_train_transform = transforms.Compose(
     [
         transforms.ToTensor(),
-        transforms.Normalize(
-            mean=[0.5, 0.5, 0.5],
-            std=[0.5, 0.5, 0.5]
-        )
+        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
     ]
 )
 
 _default_eval_transform = transforms.Compose(
     [
         transforms.ToTensor(),
-        transforms.Normalize(
-            mean=[0.5, 0.5, 0.5],
-            std=[0.5, 0.5, 0.5]
-        )
+        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
     ]
 )
 
@@ -55,7 +51,8 @@ def build_concon_scenario(
 
         for i in range(1, len(list_train_dataset)):
             new_list_train_dataset[0] = new_list_train_dataset[0].concat(
-                list_train_dataset[i])
+                list_train_dataset[i]
+            )
 
         list_train_dataset = new_list_train_dataset
 
@@ -64,14 +61,12 @@ def build_concon_scenario(
 
         for i in range(1, len(list_test_dataset)):
             new_list_test_dataset[0] = new_list_test_dataset[0].concat(
-                list_test_dataset[i])
+                list_test_dataset[i]
+            )
 
         list_test_dataset = new_list_test_dataset
 
-    return benchmark_from_datasets(
-        train=list_train_dataset,
-        test=list_test_dataset
-    )
+    return benchmark_from_datasets(train=list_train_dataset, test=list_test_dataset)
 
 
 def ConConDisjoint(
@@ -85,7 +80,7 @@ def ConConDisjoint(
 ) -> CLScenario:
     """
     Creates a ConCon Disjoint benchmark.
-    
+
     If the dataset is not present in the computer, this method will
     automatically download and store it.
 
@@ -107,20 +102,20 @@ def ConConDisjoint(
 
     :returns: The ConCon Disjoint benchmark.
     """
-    assert n_experiences == 3 or n_experiences == 1, "n_experiences must be 1 or 3 for ConCon Disjoint"
+    assert (
+        n_experiences == 3 or n_experiences == 1
+    ), "n_experiences must be 1 or 3 for ConCon Disjoint"
     list_train_dataset = []
     list_test_dataset = []
 
     for i in range(3):
         train_dataset = ConConDataset("disjoint", i, root=dataset_root, train=True)
         test_dataset = ConConDataset("disjoint", i, root=dataset_root, train=False)
         train_dataset = _as_taskaware_supervised_classification_dataset(
-            train_dataset,
-            transform=train_transform
+            train_dataset, transform=train_transform
         )
         test_dataset = _as_taskaware_supervised_classification_dataset(
-            test_dataset,
-            transform=eval_transform
+            test_dataset, transform=eval_transform
         )
         list_train_dataset.append(train_dataset)
         list_test_dataset.append(test_dataset)
@@ -130,7 +125,7 @@ def ConConDisjoint(
         list_test_dataset,
         seed=seed,
         n_experiences=n_experiences,
-        shuffle_order=shuffle_order
+        shuffle_order=shuffle_order,
     )
 
 
@@ -145,13 +140,13 @@ def ConConStrict(
 ) -> CLScenario:
     """
     Creates a ConCon Strict benchmark.
-    
+
     If the dataset is not present in the computer, this method will
     automatically download and store it.
 
     The returned benchmark will be a domain-incremental one, where each task
     is a different domain with different confounders. In this setting,
-    task-specific confounders may appear in other tasks as random features 
+    task-specific confounders may appear in other tasks as random features
     in both positive and negative samples.
 
     The benchmark instance returned by this method will have two fields,
@@ -168,20 +163,20 @@ def ConConStrict(
 
     :returns: The ConCon Strict benchmark.
     """
-    assert n_experiences == 3 or n_experiences == 1, "n_experiences must be 1 or 3 for ConCon Disjoint"
+    assert (
+        n_experiences == 3 or n_experiences == 1
+    ), "n_experiences must be 1 or 3 for ConCon Disjoint"
     list_train_dataset = []
     list_test_dataset = []
 
     for i in range(3):
         train_dataset = ConConDataset("strict", i, root=dataset_root, train=True)
         test_dataset = ConConDataset("strict", i, root=dataset_root, train=False)
         train_dataset = _as_taskaware_supervised_classification_dataset(
-            train_dataset,
-            transform=train_transform
+            train_dataset, transform=train_transform
         )
         test_dataset = _as_taskaware_supervised_classification_dataset(
-            test_dataset,
-            transform=eval_transform
+            test_dataset, transform=eval_transform
         )
         list_train_dataset.append(train_dataset)
         list_test_dataset.append(test_dataset)
@@ -191,7 +186,7 @@ def ConConStrict(
         list_test_dataset,
         seed=seed,
         n_experiences=n_experiences,
-        shuffle_order=shuffle_order
+        shuffle_order=shuffle_order,
     )
 
 
@@ -203,7 +198,7 @@ def ConConUnconfounded(
 ) -> CLScenario:
     """
     Creates a ConCon Unconfounded benchmark.
-    
+
     If the dataset is not present in the computer, this method will
     automatically download and store it.
 
@@ -214,35 +209,32 @@ def ConConUnconfounded(
     `train_stream` and `test_stream`, which can be iterated to obtain
     training and test :class:`Experience`. Each Experience contains the
     `dataset` and the associated task label.
-    
+
     :param dataset_root: The root directory of the dataset.
     :param train_transform: The training transform to use.
     :param eval_transform: The evaluation transform to use.
-    
+
     :returns: The ConCon Unconfounded benchmark.
     """
     train_dataset = []
     test_dataset = []
 
-    train_dataset.append(ConConDataset(
-        "unconfounded", 0, root=dataset_root, train=True))
-    test_dataset.append(ConConDataset(
-        "unconfounded", 0, root=dataset_root, train=False))
+    train_dataset.append(
+        ConConDataset("unconfounded", 0, root=dataset_root, train=True)
+    )
+    test_dataset.append(
+        ConConDataset("unconfounded", 0, root=dataset_root, train=False)
+    )
 
     train_dataset[0] = _as_taskaware_supervised_classification_dataset(
-        train_dataset[0],
-        transform=train_transform
+        train_dataset[0], transform=train_transform
     )
 
     test_dataset[0] = _as_taskaware_supervised_classification_dataset(
-        test_dataset[0],
-        transform=eval_transform
+        test_dataset[0], transform=eval_transform
     )
 
-    return benchmark_from_datasets(
-        train=train_dataset,
-        test=test_dataset
-    )
+    return benchmark_from_datasets(train=train_dataset, test=test_dataset)
 
 
 __all__ = [

avalanche/benchmarks/datasets/__init__.py

@@ -12,4 +12,4 @@
 from .inaturalist import *
 from .penn_fudan import *
 from .clear import *
-from .concon import *
+from .concon import *

avalanche/benchmarks/datasets/concon/__init__.py

@@ -1 +1 @@
-from .concon import *
+from .concon import *

avalanche/benchmarks/datasets/concon/concon.py

@@ -24,8 +24,8 @@ class ConConDataset(SimpleDownloadableDataset):
       and negative samples.
     - Unconfounded: No task-specific confounders.
 
-    Reference: 
-    Busch, Florian Peter, et al. "Where is the Truth? The Risk of Getting Confounded in a Continual World." 
+    Reference:
+    Busch, Florian Peter, et al. "Where is the Truth? The Risk of Getting Confounded in a Continual World."
     arXiv preprint arXiv:2402.06434 (2024).
 
     Args:
@@ -38,52 +38,58 @@ class ConConDataset(SimpleDownloadableDataset):
         transform: A function/transform that takes in an PIL image and returns a transformed version.
             E.g, ``transforms.RandomCrop`` for data augmentation.
     """
-    
+
     urls = {
         "strict": "https://zenodo.org/records/10630482/files/case_strict_main.zip",
         "disjoint": "https://zenodo.org/records/10630482/files/case_disjoint_main.zip",
-        "unconfounded": "https://zenodo.org/records/10630482/files/unconfounded.zip"
+        "unconfounded": "https://zenodo.org/records/10630482/files/unconfounded.zip",
     }
 
-    def __init__(self,
-                 variant: str,
-                 scenario: int,
-                 root: Optional[Union[str, Path]] = None,
-                 train: bool = True,
-                 download: bool = True,
-                 transform = None,
-                 ):
-        assert variant in ["strict", "disjoint", "unconfounded"], "Invalid variant, must be one of 'strict', 'disjoint', 'unconf'"
-        assert scenario in range(
-            0, 3), "Invalid scenario, must be between 0 and 2"
-        assert variant != "unconfounded" or scenario == 0, "Unconfounded scenario only has one variant"
+    def __init__(
+        self,
+        variant: str,
+        scenario: int,
+        root: Optional[Union[str, Path]] = None,
+        train: bool = True,
+        download: bool = True,
+        transform=None,
+    ):
+        assert variant in [
+            "strict",
+            "disjoint",
+            "unconfounded",
+        ], "Invalid variant, must be one of 'strict', 'disjoint', 'unconf'"
+        assert scenario in range(0, 3), "Invalid scenario, must be between 0 and 2"
+        assert (
+            variant != "unconfounded" or scenario == 0
+        ), "Unconfounded scenario only has one variant"
 
         if root is None:
             root = default_dataset_location("concon")
-                 
+
         self.root = Path(root)
-            
+
         url = self.urls[variant]
-        
+
         super(ConConDataset, self).__init__(
             self.root, url, None, download=download, verbose=True
         )
-        
+
         if variant == "strict":
             self.variant = "case_strict_main"
         elif variant == "disjoint":
             self.variant = "case_disjoint_main"
         else:
             self.variant = variant
-                    
+
         self.scenario = scenario
         self.train = train
         self.transform = transform
         self._load_dataset()
-                
+
     def _load_metadata(self) -> bool:
         root = self.root / self.variant
-        
+
         if self.train:
             images_dir = root / "train"
         else:
@@ -98,7 +104,7 @@ def _load_metadata(self) -> bool:
             for image_path in class_dir.iterdir():
                 self.image_paths.append(image_path)
                 self.targets.append(class_id)
-                
+
         return True
 
     def __len__(self):
@@ -107,14 +113,14 @@ def __len__(self):
     def __getitem__(self, idx):
         image_path = self.image_paths[idx]
         image = Image.open(image_path).convert("RGB")
-        
+
         if self.transform is not None:
             image = self.transform(image)
-        
+
         target = self.targets[idx]
         return image, target
-    
-    
+
+
 if __name__ == "__main__":
     # this little example script can be used to visualize the first image
     # loaded from the dataset.

avalanche/benchmarks/scenarios/deprecated/classification_scenario.py

@@ -257,12 +257,10 @@ def __len__(self) -> int:
         return len(self._benchmark.streams[self._stream])
 
     @overload
-    def __getitem__(self, exp_id: int) -> Optional[Set[int]]:
-        ...
+    def __getitem__(self, exp_id: int) -> Optional[Set[int]]: ...
 
     @overload
-    def __getitem__(self, exp_id: slice) -> Tuple[Optional[Set[int]], ...]:
-        ...
+    def __getitem__(self, exp_id: slice) -> Tuple[Optional[Set[int]], ...]: ...
 
     def __getitem__(self, exp_id: Union[int, slice]) -> LazyClassesInExpsRet:
         indexing_collate = _LazyClassesInClassificationExps._slice_collate

avalanche/benchmarks/scenarios/deprecated/dataset_scenario.py

@@ -184,17 +184,17 @@ def __init__(
             invoking the super constructor) to specialize the experience class.
         """
 
-        self.experience_factory: Callable[
-            [TCLStream, int], TDatasetExperience
-        ] = experience_factory
+        self.experience_factory: Callable[[TCLStream, int], TDatasetExperience] = (
+            experience_factory
+        )
 
-        self.stream_factory: Callable[
-            [str, TDatasetScenario], TCLStream
-        ] = stream_factory
+        self.stream_factory: Callable[[str, TDatasetScenario], TCLStream] = (
+            stream_factory
+        )
 
-        self.stream_definitions: Dict[
-            str, StreamDef[TCLDataset]
-        ] = DatasetScenario._check_stream_definitions(stream_definitions)
+        self.stream_definitions: Dict[str, StreamDef[TCLDataset]] = (
+            DatasetScenario._check_stream_definitions(stream_definitions)
+        )
         """
         A structure containing the definition of the streams.
         """