Merge branch 'master' of https://github.com/ContinualAI/avalanche int…

…o remove_conda_files

avalanche/benchmarks/classic/cimagenet.py

@@ -52,7 +52,8 @@ def SplitImageNet(
     class_ids_from_zero_in_each_exp: bool = False,
     class_ids_from_zero_from_first_exp: bool = False,
     train_transform: Optional[Any] = _default_train_transform,
-    eval_transform: Optional[Any] = _default_eval_transform
+    eval_transform: Optional[Any] = _default_eval_transform,
+    meta_root: Optional[Union[str, Path]] = None,
 ):
     """
     Creates a CL benchmark using the ImageNet dataset.
@@ -130,11 +131,19 @@ class "34" will be mapped to "1", class "11" to "2" and so on.
         comprehensive list of possible transformations).
         If no transformation is passed, the default test transformation
         will be used.
+    :param meta_root: Directory where the `ILSVRC2012_devkit_t12.tar.gz`
+        file can be found. The first time you use this dataset, the meta file will be
+        extracted from the archive and a `meta.bin` file will be created in the `meta_root`
+        directory. Defaults to None, which means that the meta file is expected to be
+        in the path provied in the `root` argument.
+        This is an additional argument not found in the original ImageNet class
+        from the torchvision package. For more info, see the `meta_root` argument
+        in the :class:`AvalancheImageNet` class.
 
     :returns: A properly initialized :class:`NCScenario` instance.
     """
 
-    train_set, test_set = _get_imagenet_dataset(dataset_root)
+    train_set, test_set = _get_imagenet_dataset(dataset_root, meta_root=meta_root)
 
     return nc_benchmark(
         train_dataset=train_set,
@@ -152,10 +161,10 @@ class "34" will be mapped to "1", class "11" to "2" and so on.
     )
 
 
-def _get_imagenet_dataset(root):
-    train_set = ImageNet(root, split="train")
+def _get_imagenet_dataset(root, meta_root=None):
+    train_set = ImageNet(root, split="train", meta_root=meta_root)
 
-    test_set = ImageNet(root, split="val")
+    test_set = ImageNet(root, split="val", meta_root=meta_root)
 
     return train_set, test_set
 

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/core50/core50.py

@@ -14,6 +14,7 @@
 import glob
 import os
 import pickle as pkl
+import dill
 from pathlib import Path
 from typing import List, Optional, Tuple, Union
 from warnings import warn
@@ -26,6 +27,7 @@
 from avalanche.benchmarks.datasets.downloadable_dataset import (
     DownloadableDataset,
 )
+from avalanche.checkpointing import constructor_based_serialization
 
 
 class CORe50Dataset(DownloadableDataset):
@@ -247,6 +249,25 @@ def CORe50(*args, **kwargs):
     return CORe50Dataset(*args, **kwargs)
 
 
+@dill.register(CORe50Dataset)
+def checkpoint_CORe50Dataset(pickler, obj: CORe50Dataset):
+    constructor_based_serialization(
+        pickler,
+        obj,
+        CORe50Dataset,
+        deduplicate=True,
+        kwargs=dict(
+            root=obj.root,
+            train=obj.train,
+            transform=obj.transform,
+            target_transform=obj.target_transform,
+            loader=obj.loader,
+            mini=obj.mini,
+            object_level=obj.object_level,
+        ),
+    )
+
+
 if __name__ == "__main__":
     # this litte example script can be used to visualize the first image
     # leaded from the dataset.

avalanche/benchmarks/datasets/cub200/cub200.py

@@ -23,6 +23,7 @@
 
 import gdown
 import os
+import dill
 from collections import OrderedDict
 from torchvision.datasets.folder import default_loader
 
@@ -31,6 +32,7 @@
     DownloadableDataset,
 )
 from avalanche.benchmarks.utils import PathsDataset
+from avalanche.checkpointing import constructor_based_serialization
 
 
 class CUB200(PathsDataset, DownloadableDataset):
@@ -178,6 +180,23 @@ def _load_metadata(self):
         return True
 
 
+@dill.register(CUB200)
+def checkpoint_CUB200(pickler, obj: CUB200):
+    constructor_based_serialization(
+        pickler,
+        obj,
+        CUB200,
+        deduplicate=True,
+        kwargs=dict(
+            root=obj.root,
+            train=obj.train,
+            transform=obj.transform,
+            target_transform=obj.target_transform,
+            loader=obj.loader,
+        ),
+    )
+
+
 if __name__ == "__main__":
     """Simple test that will start if you run this script directly"""
 

avalanche/benchmarks/datasets/external_datasets/cifar.py

@@ -2,6 +2,7 @@
 from torchvision.datasets import CIFAR100, CIFAR10
 
 from avalanche.benchmarks.datasets import default_dataset_location
+from avalanche.checkpointing import constructor_based_serialization
 
 
 def get_cifar10_dataset(dataset_root):
@@ -31,26 +32,34 @@ def load_CIFAR100(root, train, transform, target_transform):
 
 
 @dill.register(CIFAR100)
-def save_CIFAR100(pickler, obj: CIFAR100):
-    pickler.save_reduce(
-        load_CIFAR100,
-        (obj.root, obj.train, obj.transform, obj.target_transform),
-        obj=obj,
-    )
-
-
-def load_CIFAR10(root, train, transform, target_transform):
-    return CIFAR10(
-        root=root, train=train, transform=transform, target_transform=target_transform
+def checkpoint_CIFAR100(pickler, obj: CIFAR100):
+    constructor_based_serialization(
+        pickler,
+        obj,
+        CIFAR100,
+        deduplicate=True,
+        kwargs=dict(
+            root=obj.root,
+            train=obj.train,
+            transform=obj.transform,
+            target_transform=obj.target_transform,
+        ),
     )
 
 
 @dill.register(CIFAR10)
-def save_CIFAR10(pickler, obj: CIFAR10):
-    pickler.save_reduce(
-        load_CIFAR10,
-        (obj.root, obj.train, obj.transform, obj.target_transform),
-        obj=obj,
+def checkpoint_CIFAR10(pickler, obj: CIFAR10):
+    constructor_based_serialization(
+        pickler,
+        obj,
+        CIFAR10,
+        deduplicate=True,
+        kwargs=dict(
+            root=obj.root,
+            train=obj.train,
+            transform=obj.transform,
+            target_transform=obj.target_transform,
+        ),
     )
 
 

avalanche/benchmarks/datasets/external_datasets/fmnist.py

@@ -2,6 +2,7 @@
 from torchvision.datasets import FashionMNIST
 
 from avalanche.benchmarks.datasets import default_dataset_location
+from avalanche.checkpointing import constructor_based_serialization
 
 
 def get_fmnist_dataset(dataset_root):
@@ -13,18 +14,19 @@ def get_fmnist_dataset(dataset_root):
     return train_set, test_set
 
 
-def load_FashionMNIST(root, train, transform, target_transform):
-    return FashionMNIST(
-        root=root, train=train, transform=transform, target_transform=target_transform
-    )
-
-
 @dill.register(FashionMNIST)
-def save_FashionMNIST(pickler, obj: FashionMNIST):
-    pickler.save_reduce(
-        load_FashionMNIST,
-        (obj.root, obj.train, obj.transform, obj.target_transform),
-        obj=obj,
+def checkpoint_FashionMNIST(pickler, obj: FashionMNIST):
+    constructor_based_serialization(
+        pickler,
+        obj,
+        FashionMNIST,
+        deduplicate=True,
+        kwargs=dict(
+            root=obj.root,
+            train=obj.train,
+            transform=obj.transform,
+            target_transform=obj.target_transform,
+        ),
     )
 
 

avalanche/benchmarks/datasets/external_datasets/mnist.py

@@ -1,6 +1,7 @@
 import dill
 from torchvision.datasets import MNIST
 from avalanche.benchmarks.datasets import default_dataset_location
+from avalanche.checkpointing import constructor_based_serialization
 
 
 class TensorMNIST(MNIST):
@@ -35,16 +36,19 @@ def get_mnist_dataset(dataset_root):
     return train_set, test_set
 
 
-def load_MNIST(root, train, transform, target_transform):
-    return TensorMNIST(
-        root=root, train=train, transform=transform, target_transform=target_transform
-    )
-
-
 @dill.register(TensorMNIST)
-def save_MNIST(pickler, obj: TensorMNIST):
-    pickler.save_reduce(
-        load_MNIST, (obj.root, obj.train, obj.transform, obj.target_transform), obj=obj
+def checkpoint_TensorMNIST(pickler, obj: TensorMNIST):
+    constructor_based_serialization(
+        pickler,
+        obj,
+        TensorMNIST,
+        deduplicate=True,
+        kwargs=dict(
+            root=obj.root,
+            train=obj.train,
+            transform=obj.transform,
+            target_transform=obj.target_transform,
+        ),
     )
 
 

avalanche/benchmarks/datasets/imagenet/__init__.py

@@ -0,0 +1 @@
+from .imagenet import *