Merge branch 'master' into checkpoint_improvements

.github/workflows/environment-update.yml

@@ -50,8 +50,7 @@ jobs:
       - name: install conda environment
         run: |
           mamba create -n avalanche-env -y -v python=${{ matrix.python-version }} -c conda-forge &&
-          conda run -n avalanche-env --no-capture-output mamba install -y -v pytorch torchvision cpuonly -c pytorch &&
-          conda run -n avalanche-env --no-capture-output mamba env update --file environment.yml -v
+          conda run -n avalanche-env --no-capture-output pip install -r requirements.txt
       - name: python unit test
         id: unittest
         env:

avalanche/benchmarks/classic/openloris.py

@@ -14,8 +14,7 @@
 a number of configuration parameters."""
 
 from pathlib import Path
-from typing import Union, Any, Optional
-from typing_extensions import Literal
+from typing import Union, Any, Optional, Literal
 
 from avalanche.benchmarks.classic.classic_benchmarks_utils import (
     check_vision_benchmark,

avalanche/benchmarks/classic/stream51.py

@@ -9,9 +9,7 @@
 # Website: www.continualai.org                                                 #
 ################################################################################
 from pathlib import Path
-from typing import List, Optional, Union
-
-from typing_extensions import Literal
+from typing import List, Optional, Union, Literal
 
 from avalanche.benchmarks.datasets import Stream51
 from avalanche.benchmarks.scenarios.deprecated.generic_benchmark_creation import (

avalanche/benchmarks/datasets/lvis_dataset/lvis_dataset.py

@@ -13,14 +13,13 @@
 """ LVIS PyTorch Object Detection Dataset """
 
 from pathlib import Path
-from typing import Optional, Union, List, Sequence
 import dill
+from typing import Optional, Union, List, Sequence, TypedDict
 
 import torch
 from PIL import Image
 from torchvision.datasets.folder import default_loader
 from torchvision.transforms import ToTensor
-from typing_extensions import TypedDict
 
 from avalanche.benchmarks.datasets import (
     DownloadableDataset,

avalanche/benchmarks/datasets/mini_imagenet/mini_imagenet.py

@@ -67,10 +67,9 @@
 import glob
 import dill
 from pathlib import Path
-from typing import Union, List, Tuple, Dict
+from typing import Union, List, Tuple, Dict, Literal
 
 from torchvision.datasets.folder import default_loader
-from typing_extensions import Literal
 
 import PIL
 import numpy as np

avalanche/benchmarks/scenarios/deprecated/lazy_dataset_sequence.py

@@ -19,8 +19,8 @@
     Iterator,
     TypeVar,
     Union,
+    overload,
 )
-from typing_extensions import overload
 from avalanche.benchmarks.utils.data import AvalancheDataset
 from avalanche.benchmarks.utils.dataset_utils import manage_advanced_indexing
 

avalanche/benchmarks/scenarios/generic_scenario.py

@@ -27,8 +27,8 @@
     Union,
     Generic,
     overload,
+    final,
 )
-from typing_extensions import final
 
 import numpy as np
 

avalanche/benchmarks/scenarios/online.py

@@ -20,8 +20,8 @@
     TypeVar,
     Union,
     Protocol,
+    Literal,
 )
-from typing_extensions import Literal
 import warnings
 from avalanche.benchmarks.utils.data import AvalancheDataset
 from avalanche.benchmarks.utils.utils import concat_datasets

avalanche/benchmarks/utils/collate_functions.py

@@ -10,23 +10,19 @@
 ################################################################################
 
 import itertools
-from abc import ABC, abstractmethod
 from collections import defaultdict
 from typing import (
     List,
     TypeVar,
-    Generic,
     Sequence,
     Tuple,
     Dict,
     Union,
-    overload,
 )
 from typing_extensions import TypeAlias
 
 import torch
 from torch import Tensor
-from torch.utils.data import default_collate
 
 BatchT = TypeVar("BatchT")
 ExampleT = TypeVar("ExampleT")

avalanche/benchmarks/utils/dataset_definitions.py

@@ -9,10 +9,9 @@
 # Website: avalanche.continualai.org                                           #
 ################################################################################
 
-from typing import TypeVar, SupportsInt, Sequence
+from typing import TypeVar, SupportsInt, Sequence, Protocol
 
 from torch.utils.data.dataset import Dataset
-from typing_extensions import Protocol
 
 T_co = TypeVar("T_co", covariant=True)
 TTargetType = TypeVar("TTargetType")

avalanche/benchmarks/utils/dataset_utils.py

@@ -11,8 +11,7 @@
 from abc import ABC, abstractmethod
 import bisect
 import copy
-from typing import Iterator, overload
-from typing_extensions import final
+from typing import Iterator, overload, final
 import numpy as np
 from numpy import ndarray
 from torch import Tensor

avalanche/benchmarks/utils/transform_groups.py

@@ -28,8 +28,8 @@
     Union,
     Callable,
     Sequence,
+    Protocol,
 )
-from typing_extensions import Protocol
 
 from avalanche.benchmarks.utils.transforms import (
     MultiParamCompose,

avalanche/distributed/distributed_helper.py

@@ -2,13 +2,12 @@
 import pickle
 import warnings
 from io import BytesIO
-from typing import ContextManager, Optional, List, Any, Iterable, Dict, TypeVar
+from typing import ContextManager, Optional, List, Any, Iterable, Dict, TypeVar, Literal
 
 import torch
 from torch import Tensor
 from torch.nn.modules import Module
 from torch.nn.parallel import DistributedDataParallel
-from typing_extensions import Literal
 from torch.distributed import init_process_group, broadcast_object_list
 
 

avalanche/evaluation/metric_definitions.py

@@ -18,8 +18,9 @@
     List,
     Union,
     overload,
+    Literal,
+    Protocol,
 )
-from typing_extensions import Literal, Protocol
 from .metric_results import MetricValue, MetricType, AlternativeValues
 from .metric_utils import (
     get_metric_name,

avalanche/evaluation/metrics/confusion_matrix.py

@@ -10,7 +10,6 @@
 ################################################################################
 from matplotlib.figure import Figure
 from numpy import arange
-from typing_extensions import Literal
 from typing import (
     Any,
     Callable,
@@ -19,6 +18,7 @@
     Optional,
     TYPE_CHECKING,
     List,
+    Literal,
 )
 
 import wandb

avalanche/evaluation/metrics/detection.py

@@ -15,6 +15,7 @@
     Callable,
     Sequence,
     Optional,
+    Protocol,
 )
 
 from avalanche.benchmarks.utils.data import AvalancheDataset
@@ -41,7 +42,6 @@
 from json import JSONEncoder
 
 from torch.utils.data import Subset, ConcatDataset
-from typing_extensions import Protocol
 
 from avalanche.evaluation import PluginMetric
 from avalanche.evaluation.metric_results import MetricValue

avalanche/evaluation/metrics/images_samples.py

@@ -1,4 +1,4 @@
-from typing import List, TYPE_CHECKING, Tuple
+from typing import List, TYPE_CHECKING, Tuple, Literal
 
 from torch import Tensor
 from torch.utils.data import DataLoader
@@ -15,8 +15,6 @@
 )
 from avalanche.evaluation.metric_utils import get_metric_name
 
-from typing_extensions import Literal
-
 
 if TYPE_CHECKING:
     from avalanche.training.templates import SupervisedTemplate

avalanche/evaluation/metrics/labels_repartition.py

@@ -8,6 +8,7 @@
     List,
     Counter,
     overload,
+    Literal,
 )
 
 from matplotlib.figure import Figure
@@ -19,8 +20,6 @@
     default_history_repartition_image_creator,
 )
 
-from typing_extensions import Literal
-
 
 if TYPE_CHECKING:
     from avalanche.training.templates import SupervisedTemplate

avalanche/evaluation/metrics/mean_scores.py

@@ -11,7 +11,7 @@
 """
 from abc import ABC, abstractmethod
 from collections import defaultdict
-from typing import Callable, Dict, Set, TYPE_CHECKING, List, Optional, TypeVar
+from typing import Callable, Dict, Set, TYPE_CHECKING, List, Optional, TypeVar, Literal
 
 import torch
 from matplotlib.axes import Axes
@@ -26,8 +26,6 @@
 from avalanche.evaluation.metric_results import MetricValue, AlternativeValues
 
 
-from typing_extensions import Literal
-
 if TYPE_CHECKING:
     from avalanche.training.templates import SupervisedTemplate
     from avalanche.evaluation.metric_results import MetricResult

avalanche/models/bic_model.py

@@ -1,3 +1,4 @@
+from typing import Iterable, SupportsInt
 import torch
 
 
@@ -10,25 +11,28 @@ class BiasLayer(torch.nn.Module):
     Recognition. 2019"
     """
 
-    def __init__(self, device, clss):
+    def __init__(self, clss: Iterable[SupportsInt]):
         """
-        :param device: device used by the main model. 'cpu' or 'cuda'
         :param clss: list of classes of the current layer. This are use
             to identify the columns which are multiplied by the Bias
             correction Layer.
         """
         super().__init__()
-        self.alpha = torch.nn.Parameter(torch.ones(1, device=device))
-        self.beta = torch.nn.Parameter(torch.zeros(1, device=device))
+        self.alpha = torch.nn.Parameter(torch.ones(1))
+        self.beta = torch.nn.Parameter(torch.zeros(1))
 
-        self.clss = torch.Tensor(list(clss)).long().to(device)
-        self.not_clss = None
+        unique_classes = list(sorted(set(int(x) for x in clss)))
+
+        self.register_buffer("clss", torch.tensor(unique_classes, dtype=torch.long))
 
     def forward(self, x):
         alpha = torch.ones_like(x)
-        beta = torch.ones_like(x)
+        beta = torch.zeros_like(x)
 
         alpha[:, self.clss] = self.alpha
         beta[:, self.clss] = self.beta
 
         return alpha * x + beta
+
+
+__all__ = ["BiasLayer"]