Merge branch 'master' into dependabot-pip-requirements-protobuf-lte-4…

….24.2

.github/workflows/ci-testing.yml

@@ -56,7 +56,7 @@ jobs:
       DATASETS_VERBOSITY: warning
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v4
       with:

.github/workflows/docs-check.yml

@@ -24,7 +24,7 @@ jobs:
   make-docs:
     runs-on: ubuntu-20.04
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
       with:
         submodules: true
     - uses: actions/setup-python@v4
@@ -66,7 +66,7 @@ jobs:
   test-docs:
     runs-on: ubuntu-20.04
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
       with:
         submodules: true
     - uses: actions/setup-python@v4

.github/workflows/pypi-release.yml

@@ -14,7 +14,7 @@ jobs:
     runs-on: ubuntu-20.04
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - uses: actions/setup-python@v4
       with:
         python-version: 3.8

.pre-commit-config.yaml

@@ -36,7 +36,7 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.8.0
+    rev: v3.14.0
     hooks:
       - id: pyupgrade
         args: [--py38-plus]
@@ -48,20 +48,20 @@ repos:
       - id: nbstripout
 
   - repo: https://github.com/PyCQA/docformatter
-    rev: v1.7.3
+    rev: v1.7.5
     hooks:
       - id: docformatter
         additional_dependencies: [tomli]
         args: ["--in-place"]
 
   - repo: https://github.com/psf/black
-    rev: 23.3.0
+    rev: 23.9.1
     hooks:
       - id: black
         name: Format code
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.276
+    rev: v0.0.292
     hooks:
       - id: ruff
         args: ["--fix"]

docs/source/quickstart.rst

@@ -86,6 +86,7 @@ Inference is the process of generating predictions from trained models. To use a
 Here's an example of inference:
 
 .. testcode::
+    :skipif: flash.core.utilities.imports._TRANSFORMERS_GREATER_EQUAL_4_0
 
     # import our libraries
     from flash import Trainer
@@ -110,11 +111,13 @@ Here's an example of inference:
 We get the following output:
 
 .. testoutput::
+    :skipif: flash.core.utilities.imports._TRANSFORMERS_GREATER_EQUAL_4_0
     :hide:
 
     ...
 
 .. testcode::
+    :skipif: flash.core.utilities.imports._TRANSFORMERS_GREATER_EQUAL_4_0
     :hide:
 
     assert all(

requirements/base.txt

@@ -9,7 +9,7 @@ pytorch-lightning >1.8.0, <2.0.0  # strict
 pyDeprecate >0.2.0
 pandas >1.1.0, <=2.0.3
 jsonargparse[signatures] >=4.22.0, <4.23.0
-click >=7.1.2, <=8.1.6
+click >=7.1.2, <8.2.0
 protobuf <4.25.0
-fsspec[http] >=2022.5.0,<=2023.6.0
+fsspec[http] >=2022.5.0,<=2023.9.0
 lightning-utilities >=0.4.1

requirements/datatype_audio.txt

@@ -4,5 +4,5 @@ numpy <1.26
 torchaudio <=2.0.2
 torchvision <=0.15.2
 librosa >=0.8.1, <=0.10.0.post2
-transformers >=4.13.0, <=4.30.2
+transformers >=4.13.0, <4.34.0
 datasets >1.16.1, <=2.14.3

requirements/datatype_image.txt

@@ -3,11 +3,11 @@
 torchvision <=0.15.2
 timm >0.4.5, <=0.9.5  # effdet 0.3.0 depends on timm>=0.4.12
 lightning-bolts >=0.7.0, <0.8.0
-Pillow >8.0, <=10.0.0
+Pillow >8.0, <10.1.0
 albumentations >1.0.0, <=1.3.1
 pystiche >1.0.0, <=1.0.1
 ftfy >6.0.0, <=6.1.1
-regex <=2023.6.3
+regex <=2023.8.8
 sahi >=0.8.19, <0.11  # strict - Fixes compatibility with icevision
 
 icevision >0.8, <0.13.0

requirements/datatype_image_extras.txt

@@ -6,7 +6,7 @@ classy-vision <=0.7.0
 effdet <=0.4.1
 kornia >0.5.1, <=0.6.12
 learn2learn <=0.1.7; platform_system != "Windows"  # dead
-fastface <=0.1.3  # dead
+fastface <=0.1.4  # dead
 fairscale
 
 # pinned PL so we force a compatible TM version

requirements/datatype_pointcloud.txt

@@ -3,4 +3,4 @@
 open3d >=0.17.0, <0.18.0
 # torch >=1.8.0, <1.9.0
 # torchvision >0.9.0, <0.10.0
-tensorboard <=2.13.0
+tensorboard <=2.14.0

requirements/datatype_text.txt

@@ -2,10 +2,10 @@
 
 torchvision <=0.15.2
 sentencepiece >=0.1.95, <=0.1.99
-filelock <=3.12.2
-transformers >=4.13.0, <=4.30.2
-torchmetrics[text] >0.5.0, <1.1.0
+filelock <=3.12.3
+transformers >=4.13.0, <4.34.0
+torchmetrics[text] >0.5.0, <1.3.0
 datasets >=2.0.0, <=2.14.3
 sentence-transformers <=2.2.2
 ftfy <=6.1.1
-regex <=2023.6.3
+regex <=2023.8.8

requirements/datatype_video.txt

@@ -1,7 +1,7 @@
 # NOTE: all pins for latest are for CI consistency unless it is `strict`, then it is also forced in setup
 
 torchvision <=0.15.2
-Pillow >7.1, <=10.0.0
+Pillow >7.1, <10.1.0
 kornia >=0.5.1, <=0.6.12
 pytorchvideo ==0.1.5
 

requirements/serve.txt

@@ -1,13 +1,13 @@
 # NOTE: all pins for latest are for CI consistency unless it is `strict`, then it is also forced in setup
 
-pillow >9.0.0, <=10.0.0
+pillow >9.0.0, <10.1.0
 pyyaml >5.4, <=6.0.1
 cytoolz >0.11, <=0.12.2
 graphviz >=0.19, <=0.20.1
-tqdm >4.60, <=4.65.0
+tqdm >4.60, <=4.66.1
 fastapi >0.65, <=0.103.0
 pydantic >1.8.1, <2.0.0  # strict
-starlette <=0.31.0
+starlette <0.32.0
 uvicorn[standard] >=0.12.0, <=0.23.2
 aiofiles >22.1.0, <=23.1.0
 jinja2 >=3.0.0, <3.2.0

requirements/test.txt

@@ -1,7 +1,7 @@
 # NOTE: all pins for latest are for CI consistency unless it is `strict`, then it is also forced in setup
 
 coverage[toml]
-pytest ==7.4.0
+pytest ==7.4.1
 pytest-doctestplus ==0.13.0
 pytest-rerunfailures ==12.0
 pytest-forked ==1.6.0

requirements/testing_audio.txt

@@ -5,6 +5,6 @@ torchvision ==0.15.2
 
 timm >0.4.5, <=0.9.5  # effdet 0.3.0 depends on timm>=0.4.12
 lightning-bolts >=0.7.0, <0.8.0
-Pillow >8.0, <=10.0.0
+Pillow >8.0, <10.1.0
 albumentations >1.0.0, <=1.3.1
 pystiche >1.0.0, <=1.0.1

src/flash/audio/classification/data.py

@@ -42,8 +42,8 @@
 
 
 class AudioClassificationData(DataModule):
-    """The ``AudioClassificationData`` class is a :class:`~flash.core.data.data_module.DataModule` with a set of
-    class methods for loading data for audio classification."""
+    """The ``AudioClassificationData`` class is a :class:`~flash.core.data.data_module.DataModule` with a set of class
+    methods for loading data for audio classification."""
 
     input_transform_cls = AudioClassificationInputTransform
 
@@ -141,6 +141,7 @@ def from_files(
             >>> import os
             >>> _ = [os.remove(f"spectrogram_{i}.png") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_spectrogram_{i}.png") for i in range(1, 4)]
+
         """
 
         ds_kw = {
@@ -275,6 +276,7 @@ def from_folders(
             >>> import shutil
             >>> shutil.rmtree("train_folder")
             >>> shutil.rmtree("predict_folder")
+
         """
 
         ds_kw = {
@@ -365,6 +367,7 @@ def from_numpy(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         ds_kw = {
@@ -453,6 +456,7 @@ def from_tensors(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         ds_kw = {
@@ -607,6 +611,7 @@ def from_data_frame(
             >>> shutil.rmtree("predict_folder")
             >>> del train_data_frame
             >>> del predict_data_frame
+
         """
 
         ds_kw = {
@@ -854,6 +859,7 @@ def from_csv(
             >>> shutil.rmtree("predict_folder")
             >>> os.remove("train_data.tsv")
             >>> os.remove("predict_data.tsv")
+
         """
 
         ds_kw = {

src/flash/audio/speech_recognition/data.py

@@ -117,6 +117,7 @@ def from_files(
             >>> import os
             >>> _ = [os.remove(f"speech_{i}.wav") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_speech_{i}.wav") for i in range(1, 4)]
+
         """
 
         ds_kw = {"sampling_rate": sampling_rate}
@@ -302,6 +303,7 @@ def from_csv(
             >>> _ = [os.remove(f"predict_speech_{i}.wav") for i in range(1, 4)]
             >>> os.remove("train_data.tsv")
             >>> os.remove("predict_data.tsv")
+
         """
 
         ds_kw = {"input_key": input_field, "sampling_rate": sampling_rate}
@@ -424,6 +426,7 @@ def from_json(
             >>> _ = [os.remove(f"predict_speech_{i}.wav") for i in range(1, 4)]
             >>> os.remove("train_data.json")
             >>> os.remove("predict_data.json")
+
         """
 
         ds_kw = {"input_key": input_field, "sampling_rate": sampling_rate, "field": field}
@@ -570,6 +573,7 @@ def from_datasets(
             >>> import os
             >>> _ = [os.remove(f"speech_{i}.wav") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_speech_{i}.wav") for i in range(1, 4)]
+
         """
 
         ds_kw = {"sampling_rate": sampling_rate}

src/flash/audio/speech_recognition/model.py

@@ -45,6 +45,7 @@ class SpeechRecognition(Task):
         learning_rate: Learning rate to use for training, defaults to ``1e-5``.
         optimizer: Optimizer to use for training.
         lr_scheduler: The LR scheduler to use during training.
+
     """
 
     backbones: FlashRegistry = SPEECH_RECOGNITION_BACKBONES

src/flash/core/adapter.py

@@ -35,6 +35,7 @@ def from_task(cls, task: "flash.Task", **kwargs) -> "Adapter":
         """Instantiate the adapter from the given :class:`~flash.core.model.Task`.
 
         This includes resolution / creation of backbones / heads and any other provider specific options.
+
         """
 
     def forward(self, x: Any) -> Any:
@@ -73,6 +74,7 @@ class AdapterTask(Task):
     Args:
         adapter: The :class:`~flash.core.adapter.Adapter` to wrap.
         kwargs: Keyword arguments to be passed to the base :class:`~flash.core.model.Task`.
+
     """
 
     def __init__(self, adapter: Adapter, **kwargs):

src/flash/core/data/base_viz.py

@@ -96,6 +96,7 @@ def show(self, batch: Dict[str, Any], running_stage: RunningStage):
         As the :class:`~flash.core.data.io.input_transform.InputTransform` hooks are injected within
         the threaded workers of the DataLoader,
         the data won't be accessible when using ``num_workers > 0``.
+
     """
 
     def _show(

src/flash/core/data/callback.py

@@ -19,6 +19,7 @@ class FlashCallback(Callback):
     Same as PyTorch Lightning, Callbacks can be provided directly to the Trainer::
 
         trainer = Trainer(callbacks=[MyCustomCallback()])
+
     """
 
     def on_per_sample_transform(self, sample: Tensor, running_stage: RunningStage) -> None:
@@ -146,6 +147,7 @@ def from_inputs(
             'val': {},
             'predict': {}
         }
+
     """
 
     batches: dict

src/flash/core/data/data_module.py

@@ -44,8 +44,7 @@
 
 class DatasetInput(Input):
     """The ``DatasetInput`` implements default behaviours for data sources which expect the input to
-    :meth:`~flash.core.data.io.input.Input.load_data` to be a :class:`torch.utils.data.dataset.Dataset`
-    """
+    :meth:`~flash.core.data.io.input.Input.load_data` to be a :class:`torch.utils.data.dataset.Dataset`"""
 
     def load_sample(self, sample: Any) -> Dict[str, Any]:
         if isinstance(sample, tuple) and len(sample) == 2:
@@ -103,6 +102,7 @@ class DataModule(pl.LightningDataModule):
         >>> datamodule = DataModule(train_input, sampler=WeightedRandomSampler([0.1, 0.5], 2), batch_size=1)
         >>> print(datamodule.train_dataloader().sampler)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
         <torch.utils.data.sampler.WeightedRandomSampler object at ...>
+
     """
 
     input_transform_cls = InputTransform
@@ -399,6 +399,7 @@ def configure_data_fetcher(*args, **kwargs) -> BaseDataFetcher:
         """This function is used to configure a :class:`~flash.core.data.callback.BaseDataFetcher`.
 
         Override with your custom one.
+
         """
         return BaseDataFetcher()
 

src/flash/core/data/io/classification_input.py

@@ -25,6 +25,7 @@ class ClassificationInputMixin(Properties):
       targets and store metadata like ``labels`` and ``num_classes``.
     * In the ``load_sample`` method, use ``format_target`` to convert the target to a standard format for use with our
       tasks.
+
     """
 
     target_formatter: TargetFormatter
@@ -46,6 +47,7 @@ def load_target_metadata(
                 rather than inferring from the targets.
             add_background: If ``True``, a background class will be inserted as class zero if ``labels`` and
                 ``num_classes`` are being inferred.
+
         """
         self.target_formatter = target_formatter
         if target_formatter is None and targets is not None: