update docs

doc/benchmarking/custom_benchmarks.rst

@@ -40,7 +40,7 @@ with your data module as follows.
 .. code-block:: python
 
     def data_module_fn(
-        data_path: Union[Path, str], chunk_id: int, seed: int, class_groupings: Tuple[Tuple[int]]
+        data_path: str, chunk_id: int, seed: int, class_groupings: Tuple[Tuple[int]]
     ):
         data_module = CustomDataModule(data_path=data_path, seed=seed)
         return ClassIncrementalScenario(

doc/benchmarking/renate_benchmarks.rst

@@ -93,6 +93,10 @@ The full list of models and model names including a short description is provide
     * - `~renate.benchmark.models.vision_transformer.VisionTransformerH14`
       - Huge `Vision Transformer <https://arxiv.org/abs/2010.11929>`_ architecture for images of size 224x224 with patch size 14.
       - * ``num_outputs``: Output dimensionality, for classification the number of classes.
+    * - `~renate.benchmark.models.transformer.HuggingFaceSequenceClassificationTransformer`
+      - Wrapper around Hugging Face transformers.
+      - * ``pretrained_model_name``: Hugging Face `transformer ID <https://huggingface.co/models>`__.
+        * ``num_outputs``: The number of classes.
 
 
 .. _benchmarking-renate-benchmarks-datasets:
@@ -133,6 +137,10 @@ The following table contains the list of supported datasets.
       - Image Classification
       - 60k train, 10k test, 10 classes, image shape 28x28x1
       - Li Deng: The MNIST Database of Handwritten Digit Images for Machine Learning Research. IEEE Signal Processing Magazine. 2012.
+    * - hfd-{dataset_name}
+      - multiple
+      - Any `Hugging Face dataset <https://huggingface.co/datasets>`__ can be used. Just prepend the prefix ``hfd-``, e.g., ``hfd-rotten_tomatoes``. Select input and target columns via ``config_space``, e.g., add ``"input_column": "text", "target_column": "label"`` for the `rotten_tomatoes <https://huggingface.co/datasets/rotten_tomatoes>`__ example.
+      - Please refer to `the official documentation <https://huggingface.co/datasets>`__.
 
 .. _benchmarking-renate-benchmarks-scenarios:
 

doc/getting_started/how_to_renate_config.rst

@@ -16,7 +16,7 @@ Its signature is
 
 .. code-block:: python
 
-    def model_fn(model_state_url: Optional[Union[Path, str]] = None) -> RenateModule:
+    def model_fn(model_state_url: Optional[str] = None) -> RenateModule:
 
 A :py:class:`~renate.models.renate_module.RenateModule` is a :code:`torch.nn.Module` with some
 additional functionality relevant to continual learning.
@@ -29,7 +29,7 @@ method, which automatically handles model hyperparameters.
 
 .. literalinclude:: ../../examples/getting_started/renate_config.py
     :caption: Example
-    :lines: 13-39
+    :lines: 14-39
 
 If you are using a torch model with **no or fixed hyperparameters**, you can use
 :py:class:`~renate.models.renate_module.RenateWrapper`.
@@ -40,7 +40,7 @@ method, but simply reinstantiate your model and call :code:`load_state_dict`.
 .. code-block:: python
     :caption: Example
 
-    def model_fn(model_state_url: Optional[Union[Path, str]] = None) -> RenateModule:
+    def model_fn(model_state_url: Optional[str] = None) -> RenateModule:
         my_torch_model = torch.nn.Linear(28 * 28, 10)  # Instantiate your torch model.
         model = RenateWrapper(my_torch_model)
         if model_state_url is not None:
@@ -58,7 +58,7 @@ Its signature is
 
 .. code-block:: python
 
-    def data_module_fn(data_path: Union[Path, str], seed: int = defaults.SEED) -> RenateDataModule:
+    def data_module_fn(data_path: str, seed: int = defaults.SEED) -> RenateDataModule:
 
 :py:class:`~renate.data.data_module.RenateDataModule` provides a structured interface to
 download, set up, and access train/val/test datasets.
@@ -67,7 +67,7 @@ such as data subsampling or splitting.
 
 .. literalinclude:: ../../examples/getting_started/renate_config.py
     :caption: Example
-    :lines: 42-72
+    :lines: 43-69
 
 Transforms
 ==========
@@ -143,11 +143,11 @@ Let us assume we already have a config file in which we implemented a simple lin
 
 .. code-block:: python
 
-    def model_fn(model_state_url: Optional[Union[Path, str]] = None) -> RenateModule:
+    def model_fn(model_state_url: Optional[str] = None) -> RenateModule:
         my_torch_model = torch.nn.Linear(28 * 28, 10)
         model = RenateWrapper(my_torch_model)
         if model_state_url is not None:
-            state_dict = torch.load(str(model_state_url))
+            state_dict = torch.load(model_state_url)
             model.load_state_dict(state_dict)
         return model
 
@@ -156,11 +156,11 @@ The natural change would be to change it to something like
 
 .. code-block:: python
 
-    def model_fn(num_inputs: int, num_outputs: int, model_state_url: Optional[Union[Path, str]] = None) -> RenateModule:
+    def model_fn(num_inputs: int, num_outputs: int, model_state_url: Optional[str] = None) -> RenateModule:
         my_torch_model = torch.nn.Linear(num_inputs, num_outputs)
         model = RenateWrapper(my_torch_model)
         if model_state_url is not None:
-            state_dict = torch.load(str(model_state_url))
+            state_dict = torch.load(model_state_url)
             model.load_state_dict(state_dict)
         return model
 

doc/getting_started/output.rst

@@ -31,13 +31,13 @@ In the following, we refer with :code:`model_fn` to the function defined by the
 Output Saved Locally
 ~~~~~~~~~~~~~~~~~~~~
 
-If :code:`next_state_url` is a path to a local folder, loading the updated model can be done as follows:
+If :code:`output_state_url` is a path to a local folder, loading the updated model can be done as follows:
 
 .. code-block:: python
 
-    from renate.defaults import current_state_folder, model_file
+    from renate.defaults import input_state_folder, model_file
 
-    my_model = model_fn(model_file(current_state_folder(next_state_url)))
+    my_model = model_fn(model_file(input_state_folder(output_state_url)))
 
 Output Saved on S3
 ~~~~~~~~~~~~~~~~~~
@@ -46,9 +46,9 @@ If the Renate output was saved on S3, the model checkpoint :code:`model.ckpt` ca
 
 .. code-block:: python
 
-    from renate.defaults import current_state_folder, model_file
+    from renate.defaults import input_state_folder, model_file
 
-    print(model_file(current_state_folder(next_state_url)))
+    print(model_file(input_state_folder(output_state_url)))
 
 and then loaded via
 

examples/simple_classifier_cifar10/renate_config.py

@@ -1,7 +1,6 @@
 # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
 # SPDX-License-Identifier: Apache-2.0
-from pathlib import Path
-from typing import Callable, Optional, Union
+from typing import Callable, Optional
 
 import torch
 from torchvision import transforms
@@ -13,12 +12,12 @@
 from renate.models import RenateModule
 
 
-def model_fn(model_state_url: Optional[Union[Path, str]] = None) -> RenateModule:
+def model_fn(model_state_url: Optional[str] = None) -> RenateModule:
     """Returns a model instance."""
     if model_state_url is None:
         model = ResNet18CIFAR()
     else:
-        state_dict = torch.load(str(model_state_url))
+        state_dict = torch.load(model_state_url)
         model = ResNet18CIFAR.from_state_dict(state_dict)
     return model
 

examples/train_mlp_locally/renate_config.py

@@ -1,7 +1,6 @@
 # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
 # SPDX-License-Identifier: Apache-2.0
-from pathlib import Path
-from typing import Callable, Optional, Union
+from typing import Callable, Optional
 
 import torch
 from torchvision.transforms import transforms
@@ -34,14 +33,14 @@ def data_module_fn(data_path: str, chunk_id: int, seed: int = defaults.SEED) ->
     return class_incremental_scenario
 
 
-def model_fn(model_state_url: Optional[Union[Path, str]] = None) -> RenateModule:
+def model_fn(model_state_url: Optional[str] = None) -> RenateModule:
     """Returns a model instance."""
     if model_state_url is None:
         model = MultiLayerPerceptron(
             num_inputs=784, num_outputs=10, num_hidden_layers=2, hidden_size=128
         )
     else:
-        state_dict = torch.load(str(model_state_url))
+        state_dict = torch.load(model_state_url)
         model = MultiLayerPerceptron.from_state_dict(state_dict)
     return model
 

src/renate/defaults.py

@@ -120,41 +120,41 @@ def current_timestamp() -> str:
     return str(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S-%f"))
 
 
-def data_folder(working_directory: str):
+def data_folder(working_directory: str) -> str:
     return os.path.join(working_directory, "data")
 
 
-def input_state_folder(working_directory: str):
+def input_state_folder(working_directory: str) -> str:
     return os.path.join(working_directory, "input_state")
 
 
-def output_state_folder(working_directory: str):
+def output_state_folder(working_directory: str) -> str:
     return os.path.join(working_directory, "output_state")
 
 
-def logs_folder(working_directory: str):
+def logs_folder(working_directory: str) -> str:
     return os.path.join(working_directory, "logs")
 
 
-def model_file(state_folder: str):
+def model_file(state_folder: str) -> str:
     return os.path.join(state_folder, "model.ckpt")
 
 
 LEARNER_CHECKPOINT_NAME = "learner.ckpt"
 AVALANCHE_CHECKPOINT_NAME = "avalanche.ckpt"
 
 
-def learner_state_file(state_folder: str):
+def learner_state_file(state_folder: str) -> str:
     return os.path.join(state_folder, LEARNER_CHECKPOINT_NAME)
 
 
-def avalanche_state_file(state_folder: str):
+def avalanche_state_file(state_folder: str) -> str:
     return os.path.join(state_folder, AVALANCHE_CHECKPOINT_NAME)
 
 
-def metric_summary_file(logs_folder: str, special_str: str = ""):
+def metric_summary_file(logs_folder: str, special_str: str = "") -> str:
     return os.path.join(logs_folder, f"metrics_summary{special_str}.csv")
 
 
-def hpo_file(state_folder: str):
+def hpo_file(state_folder: str) -> str:
     return os.path.join(state_folder, "hpo.csv")