switch to black formatting

.flake8

@@ -0,0 +1,5 @@
+[flake8]
+ignore = E203, E266, E501, W503, F403, F401
+max-line-length = 89
+max-complexity = 18
+select = B,C,E,F,W,T4,B9

.github/workflows/black.yaml

@@ -0,0 +1,10 @@
+name: Lint
+
+on: [push, pull_request]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: psf/black@stable

.gitignore

@@ -14,4 +14,4 @@ csvlogs/
 docs/generated/
 .fleet
 pip-wheel-metadata
-**/.DS_Store
+**/.DS_Store

avalanche/_annotations.py

@@ -34,7 +34,7 @@ def shining_new_method():
 
     def decorator(func):
         if func.__doc__ is None:
-            func.__doc__ = ''
+            func.__doc__ = ""
         else:
             func.__doc__ += "\n\n"
 
@@ -59,6 +59,7 @@ def deprecated(version: float, reason: str):
         alternative
     :return:
     """
+
     def decorator(func):
         if inspect.isclass(func):
             msg_prefix = "Call to deprecated class {name}"
@@ -69,21 +70,23 @@ def decorator(func):
         msg = msg_prefix + msg_suffix
 
         if func.__doc__ is None:
-            func.__doc__ = ''
+            func.__doc__ = ""
         else:
             func.__doc__ += "\n\n"
 
         func.__doc__ += "Warning: Deprecated" + msg_suffix.format(
-            name=func.__name__, version=version, reason=reason)
+            name=func.__name__, version=version, reason=reason
+        )
 
         @functools.wraps(func)
         def wrapper(*args, **kwargs):
-            warnings.simplefilter('always', DeprecationWarning)
-            warnings.warn(msg.format(name=func.__name__, version=version,
-                                     reason=reason),
-                          category=DeprecationWarning,
-                          stacklevel=2)
-            warnings.simplefilter('default', DeprecationWarning)
+            warnings.simplefilter("always", DeprecationWarning)
+            warnings.warn(
+                msg.format(name=func.__name__, version=version, reason=reason),
+                category=DeprecationWarning,
+                stacklevel=2,
+            )
+            warnings.simplefilter("default", DeprecationWarning)
             return func(*args, **kwargs)
 
         return wrapper

avalanche/benchmarks/classic/ccifar10.py

@@ -18,26 +18,21 @@
     check_vision_benchmark,
 )
 
-from avalanche.benchmarks.datasets.external_datasets.cifar import \
-    get_cifar10_dataset
+from avalanche.benchmarks.datasets.external_datasets.cifar import get_cifar10_dataset
 
 _default_cifar10_train_transform = transforms.Compose(
     [
         transforms.RandomCrop(32, padding=4),
         transforms.RandomHorizontalFlip(),
         transforms.ToTensor(),
-        transforms.Normalize(
-            (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
-        ),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
     ]
 )
 
 _default_cifar10_eval_transform = transforms.Compose(
     [
         transforms.ToTensor(),
-        transforms.Normalize(
-            (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
-        ),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
     ]
 )
 
@@ -158,6 +153,4 @@ class "34" will be mapped to "1", class "11" to "2" and so on.
     check_vision_benchmark(benchmark_instance)
     sys.exit(0)
 
-__all__ = [
-    "SplitCIFAR10"
-]
+__all__ = ["SplitCIFAR10"]

avalanche/benchmarks/classic/ccifar100.py

@@ -19,10 +19,13 @@
     check_vision_benchmark,
 )
 
-from avalanche.benchmarks.datasets.external_datasets.cifar import \
-    get_cifar100_dataset, get_cifar10_dataset
-from avalanche.benchmarks.utils.classification_dataset import \
-    concat_classification_datasets_sequentially
+from avalanche.benchmarks.datasets.external_datasets.cifar import (
+    get_cifar100_dataset,
+    get_cifar10_dataset,
+)
+from avalanche.benchmarks.utils.classification_dataset import (
+    concat_classification_datasets_sequentially,
+)
 
 from avalanche.benchmarks import nc_benchmark, NCScenario
 
@@ -31,18 +34,14 @@
         transforms.RandomCrop(32, padding=4),
         transforms.RandomHorizontalFlip(),
         transforms.ToTensor(),
-        transforms.Normalize(
-            (0.5071, 0.4865, 0.4409), (0.2673, 0.2564, 0.2762)
-        ),
+        transforms.Normalize((0.5071, 0.4865, 0.4409), (0.2673, 0.2564, 0.2762)),
     ]
 )
 
 _default_cifar100_eval_transform = transforms.Compose(
     [
         transforms.ToTensor(),
-        transforms.Normalize(
-            (0.5071, 0.4865, 0.4409), (0.2673, 0.2564, 0.2762)
-        ),
+        transforms.Normalize((0.5071, 0.4865, 0.4409), (0.2673, 0.2564, 0.2762)),
     ]
 )
 
@@ -242,10 +241,13 @@ class "34" will be mapped to "1", class "11" to "2" and so on.
     cifar10_train, cifar10_test = get_cifar10_dataset(dataset_root_cifar10)
     cifar100_train, cifar100_test = get_cifar100_dataset(dataset_root_cifar100)
 
-    cifar_10_100_train, cifar_10_100_test, _ = \
-        concat_classification_datasets_sequentially(
-            [cifar10_train, cifar100_train], [cifar10_test, cifar100_test]
-        )
+    (
+        cifar_10_100_train,
+        cifar_10_100_test,
+        _,
+    ) = concat_classification_datasets_sequentially(
+        [cifar10_train, cifar100_train], [cifar10_test, cifar100_test]
+    )
     # cifar10 classes
     class_order = [_ for _ in range(10)]
     # if a class order is defined (for cifar100) the given class labels are
@@ -288,7 +290,4 @@ class "34" will be mapped to "1", class "11" to "2" and so on.
     sys.exit(0)
 
 
-__all__ = [
-    "SplitCIFAR100",
-    "SplitCIFAR110"
-]
+__all__ = ["SplitCIFAR100", "SplitCIFAR110"]

avalanche/benchmarks/classic/ccub200.py

@@ -26,18 +26,14 @@
     [
         transforms.RandomHorizontalFlip(),
         transforms.ToTensor(),
-        transforms.Normalize(
-            (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
-        ),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
     ]
 )
 
 _default_eval_transform = transforms.Compose(
     [
         transforms.ToTensor(),
-        transforms.Normalize(
-            (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
-        ),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
     ]
 )
 

avalanche/benchmarks/classic/cfashion_mnist.py

@@ -19,8 +19,7 @@
 from avalanche.benchmarks.classic.classic_benchmarks_utils import (
     check_vision_benchmark,
 )
-from avalanche.benchmarks.datasets.external_datasets.fmnist import \
-    get_fmnist_dataset
+from avalanche.benchmarks.datasets.external_datasets.fmnist import get_fmnist_dataset
 
 _default_fmnist_train_transform = transforms.Compose(
     [transforms.ToTensor(), transforms.Normalize((0.2860,), (0.3530,))]

avalanche/benchmarks/classic/cimagenet.py

@@ -19,9 +19,7 @@
 
 from torchvision import transforms
 
-normalize = transforms.Normalize(
-    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-)
+normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
 
 _default_train_transform = transforms.Compose(
     [

avalanche/benchmarks/classic/cinaturalist.py

@@ -22,9 +22,7 @@
 
 from torchvision import transforms
 
-normalize = transforms.Normalize(
-    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-)
+normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
 
 _default_train_transform = transforms.Compose(
     [
@@ -181,16 +179,10 @@ def _get_inaturalist_dataset(dataset_root, super_categories, download):
         dataset_root = default_dataset_location("inatuarlist2018")
 
     train_set = INATURALIST2018(
-        str(dataset_root),
-        split="train",
-        supcats=super_categories,
-        download=download
+        str(dataset_root), split="train", supcats=super_categories, download=download
     )
     test_set = INATURALIST2018(
-        str(dataset_root),
-        split="val",
-        supcats=super_categories,
-        download=download
+        str(dataset_root), split="val", supcats=super_categories, download=download
     )
 
     return train_set, test_set

avalanche/benchmarks/classic/classic_benchmarks_utils.py

@@ -4,14 +4,13 @@
     DatasetStream,
 )
 
-from avalanche.benchmarks.utils.classification_dataset import \
-    ClassificationDataset
+from avalanche.benchmarks.utils.classification_dataset import ClassificationDataset
 from avalanche.benchmarks.utils.data import AvalancheDataset
 
 
 def check_vision_benchmark(
-        benchmark_instance: DatasetScenario,
-        show_without_transforms=True):
+    benchmark_instance: DatasetScenario, show_without_transforms=True
+):
     from matplotlib import pyplot as plt
     from torch.utils.data.dataloader import DataLoader
 

avalanche/benchmarks/classic/clear.py

@@ -118,8 +118,7 @@ def CLEAR(
 
     if evaluation_protocol == "streaming":
         assert seed is None, (
-            "Seed for train/test split is not required "
-            "under streaming protocol"
+            "Seed for train/test split is not required " "under streaming protocol"
         )
         train_split = "all"
         test_split = "all"
@@ -300,7 +299,7 @@ def backward_transfer(self, matrix):
             seed_list = [None]
         else:
             seed_list = SEED_LIST
-        
+
         for f in [None] + CLEAR_FEATURE_TYPES[data_name]:
             t = transform if f is None else None
             for seed in seed_list: