[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

docs/source/conf.py

@@ -17,7 +17,7 @@
 import pt_lightning_sphinx_theme
 
 _PATH_HERE = os.path.abspath(os.path.dirname(__file__))
-_PATH_ROOT = os.path.join(_PATH_HERE, '..', '..')
+_PATH_ROOT = os.path.join(_PATH_HERE, "..", "..")
 sys.path.insert(0, os.path.abspath(_PATH_ROOT))
 
 try:
@@ -33,9 +33,9 @@ def _load_py_module(fname, pkg="flash"):
 
     about = _load_py_module("__about__.py")
 
-SPHINX_MOCK_REQUIREMENTS = int(os.environ.get('SPHINX_MOCK_REQUIREMENTS', True))
+SPHINX_MOCK_REQUIREMENTS = int(os.environ.get("SPHINX_MOCK_REQUIREMENTS", True))
 
-html_favicon = '_static/images/icon.svg'
+html_favicon = "_static/images/icon.svg"
 
 # -- Project information -----------------------------------------------------
 
@@ -49,22 +49,22 @@ def _load_py_module(fname, pkg="flash"):
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.doctest',
-    'sphinx.ext.intersphinx',
-    'sphinx.ext.todo',
+    "sphinx.ext.autodoc",
+    "sphinx.ext.doctest",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.todo",
     # 'sphinx.ext.coverage',
-    'sphinx.ext.viewcode',
-    'sphinx.ext.autosummary',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.imgmath',
-    'recommonmark',
+    "sphinx.ext.viewcode",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.imgmath",
+    "recommonmark",
     # 'sphinx.ext.autosectionlabel',
     # 'nbsphinx',  # it seems some sphinx issue
-    'sphinx_autodoc_typehints',
-    'sphinx_copybutton',
-    'sphinx_paramlinks',
-    'sphinx_togglebutton',
+    "sphinx_autodoc_typehints",
+    "sphinx_copybutton",
+    "sphinx_paramlinks",
+    "sphinx_togglebutton",
 ]
 
 # autodoc: Default to members and undoc-members
@@ -114,8 +114,8 @@ def _load_py_module(fname, pkg="flash"):
 # documentation.
 
 html_theme_options = {
-    'pytorch_project': 'https://pytorchlightning.ai',
-    'canonical_url': about.__docs_url__,
+    "pytorch_project": "https://pytorchlightning.ai",
+    "canonical_url": about.__docs_url__,
     "collapse_navigation": False,
     "display_version": True,
     "logo_only": False,
@@ -132,47 +132,47 @@ def _load_py_module(fname, pkg="flash"):
 def setup(app):
     # this is for hiding doctest decoration,
     # see: http://z4r.github.io/python/2011/12/02/hides-the-prompts-and-output/
-    app.add_js_file('copybutton.js')
-    app.add_css_file('main.css')
+    app.add_js_file("copybutton.js")
+    app.add_css_file("main.css")
 
 
 # Ignoring Third-party packages
 # https://stackoverflow.com/questions/15889621/sphinx-how-to-exclude-imports-in-automodule
 def _package_list_from_file(pfile):
     assert os.path.isfile(pfile)
-    with open(pfile, 'r') as fp:
+    with open(pfile, "r") as fp:
         lines = fp.readlines()
     list_pkgs = []
     for ln in lines:
-        found = [ln.index(ch) for ch in list(',=<>#@') if ch in ln]
-        pkg = ln[:min(found)] if found else ln
+        found = [ln.index(ch) for ch in list(",=<>#@") if ch in ln]
+        pkg = ln[: min(found)] if found else ln
         if pkg.strip():
             list_pkgs.append(pkg.strip())
     return list_pkgs
 
 
 # define mapping from PyPI names to python imports
 PACKAGE_MAPPING = {
-    'pytorch-lightning': 'pytorch_lightning',
-    'scikit-learn': 'sklearn',
-    'Pillow': 'PIL',
-    'PyYAML': 'yaml',
-    'rouge-score': 'rouge_score',
-    'lightning-bolts': 'pl_bolts',
-    'pytorch-tabnet': 'pytorch_tabnet',
-    'pyDeprecate': 'deprecate',
+    "pytorch-lightning": "pytorch_lightning",
+    "scikit-learn": "sklearn",
+    "Pillow": "PIL",
+    "PyYAML": "yaml",
+    "rouge-score": "rouge_score",
+    "lightning-bolts": "pl_bolts",
+    "pytorch-tabnet": "pytorch_tabnet",
+    "pyDeprecate": "deprecate",
 }
 MOCK_PACKAGES = []
 if SPHINX_MOCK_REQUIREMENTS:
     # mock also base packages when we are on RTD since we don't install them there
-    MOCK_PACKAGES += _package_list_from_file(os.path.join(_PATH_ROOT, 'requirements.txt'))
+    MOCK_PACKAGES += _package_list_from_file(os.path.join(_PATH_ROOT, "requirements.txt"))
 # replace PyPI packages by importing ones
 MOCK_PACKAGES = [PACKAGE_MAPPING.get(pkg, pkg) for pkg in MOCK_PACKAGES]
 
 autodoc_mock_imports = MOCK_PACKAGES
 
 # only run doctests marked with a ".. doctest::" directive
-doctest_test_doctest_blocks = ''
+doctest_test_doctest_blocks = ""
 doctest_global_setup = """
 import torch
 import pytorch_lightning as pl

flash/__about__.py

@@ -1,7 +1,7 @@
 __version__ = "0.4.1dev"
 __author__ = "PyTorchLightning et al."
 __author_email__ = "[email protected]"
-__license__ = 'Apache-2.0'
+__license__ = "Apache-2.0"
 __copyright__ = f"Copyright (c) 2020-2021, f{__author__}."
 __homepage__ = "https://github.com/PyTorchLightning/lightning-flash"
 __docs_url__ = "https://lightning-flash.readthedocs.io/en/stable/"

flash/__init__.py

@@ -33,6 +33,7 @@
 
     if _IS_TESTING:
         from pytorch_lightning import seed_everything
+
         seed_everything(42)
 
     __all__ = [

flash/__main__.py

@@ -24,15 +24,16 @@ def main():
 
 
 def register_command(command):
-
-    @main.command(context_settings=dict(
-        help_option_names=[],
-        ignore_unknown_options=True,
-    ))
-    @click.argument('cli_args', nargs=-1, type=click.UNPROCESSED)
+    @main.command(
+        context_settings=dict(
+            help_option_names=[],
+            ignore_unknown_options=True,
+        )
+    )
+    @click.argument("cli_args", nargs=-1, type=click.UNPROCESSED)
     @functools.wraps(command)
     def wrapper(cli_args):
-        with patch('sys.argv', [command.__name__] + list(cli_args)):
+        with patch("sys.argv", [command.__name__] + list(cli_args)):
             command()
 
 
@@ -63,5 +64,5 @@ def wrapper(cli_args):
     except ImportError:
         pass
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

flash/audio/classification/cli.py

@@ -44,12 +44,12 @@ def audio_classification():
         AudioClassificationData,
         default_datamodule_builder=from_urban8k,
         default_arguments={
-            'trainer.max_epochs': 3,
-        }
+            "trainer.max_epochs": 3,
+        },
     )
 
     cli.trainer.save_checkpoint("audio_classification_model.pt")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     audio_classification()

flash/audio/classification/data.py

@@ -24,7 +24,6 @@
 
 
 class AudioClassificationPreprocess(Preprocess):
-
     @requires_extras(["audio", "image"])
     def __init__(
         self,
@@ -35,7 +34,7 @@ def __init__(
         spectrogram_size: Tuple[int, int] = (196, 196),
         time_mask_param: int = 80,
         freq_mask_param: int = 80,
-        deserializer: Optional['Deserializer'] = None,
+        deserializer: Optional["Deserializer"] = None,
     ):
         self.spectrogram_size = spectrogram_size
         self.time_mask_param = time_mask_param
@@ -48,7 +47,7 @@ def __init__(
             predict_transform=predict_transform,
             data_sources={
                 DefaultDataSources.FILES: ImagePathsDataSource(),
-                DefaultDataSources.FOLDERS: ImagePathsDataSource()
+                DefaultDataSources.FOLDERS: ImagePathsDataSource(),
             },
             deserializer=deserializer or ImageDeserializer(),
             default_data_source=DefaultDataSources.FILES,

flash/audio/classification/transforms.py

@@ -41,13 +41,14 @@ def default_transforms(spectrogram_size: Tuple[int, int]) -> Dict[str, Callable]
     }
 
 
-def train_default_transforms(spectrogram_size: Tuple[int, int], time_mask_param: int,
-                             freq_mask_param: int) -> Dict[str, Callable]:
+def train_default_transforms(
+    spectrogram_size: Tuple[int, int], time_mask_param: int, freq_mask_param: int
+) -> Dict[str, Callable]:
     """During training we apply the default transforms with additional ``TimeMasking`` and ``Frequency Masking``"""
     transforms = {
         "post_tensor_transform": nn.Sequential(
             ApplyToKeys(DefaultDataKeys.INPUT, TAudio.TimeMasking(time_mask_param=time_mask_param)),
-            ApplyToKeys(DefaultDataKeys.INPUT, TAudio.FrequencyMasking(freq_mask_param=freq_mask_param))
+            ApplyToKeys(DefaultDataKeys.INPUT, TAudio.FrequencyMasking(freq_mask_param=freq_mask_param)),
         )
     }
 

flash/audio/speech_recognition/cli.py

@@ -47,13 +47,13 @@ def speech_recognition():
         SpeechRecognitionData,
         default_datamodule_builder=from_timit,
         default_arguments={
-            'trainer.max_epochs': 3,
+            "trainer.max_epochs": 3,
         },
         finetune=False,
     )
 
     cli.trainer.save_checkpoint("speech_recognition_model.pt")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     speech_recognition()

flash/audio/speech_recognition/data.py

@@ -44,17 +44,14 @@
 
 
 class SpeechRecognitionDeserializer(Deserializer):
-
     def deserialize(self, sample: Any) -> Dict:
         encoded_with_padding = (sample + "===").encode("ascii")
         audio = base64.b64decode(encoded_with_padding)
         buffer = io.BytesIO(audio)
         data, sampling_rate = sf.read(buffer)
         return {
             DefaultDataKeys.INPUT: data,
-            DefaultDataKeys.METADATA: {
-                "sampling_rate": sampling_rate
-            },
+            DefaultDataKeys.METADATA: {"sampling_rate": sampling_rate},
         }
 
     @property
@@ -64,11 +61,13 @@ def example_input(self) -> str:
 
 
 class BaseSpeechRecognition:
-
     def _load_sample(self, sample: Dict[str, Any]) -> Any:
         path = sample[DefaultDataKeys.INPUT]
-        if not os.path.isabs(path) and DefaultDataKeys.METADATA in sample and "root" in sample[DefaultDataKeys.METADATA
-                                                                                               ]:
+        if (
+            not os.path.isabs(path)
+            and DefaultDataKeys.METADATA in sample
+            and "root" in sample[DefaultDataKeys.METADATA]
+        ):
             path = os.path.join(sample[DefaultDataKeys.METADATA]["root"], path)
         speech_array, sampling_rate = sf.read(path)
         sample[DefaultDataKeys.INPUT] = speech_array
@@ -77,7 +76,6 @@ def _load_sample(self, sample: Dict[str, Any]) -> Any:
 
 
 class SpeechRecognitionFileDataSource(DataSource, BaseSpeechRecognition):
-
     def __init__(self, filetype: Optional[str] = None):
         super().__init__()
         self.filetype = filetype
@@ -87,50 +85,49 @@ def load_data(
         data: Tuple[str, Union[str, List[str]], Union[str, List[str]]],
         dataset: Optional[Any] = None,
     ) -> Union[Sequence[Mapping[str, Any]]]:
-        if self.filetype == 'json':
+        if self.filetype == "json":
             file, input_key, target_key, field = data
         else:
             file, input_key, target_key = data
         stage = self.running_stage.value
-        if self.filetype == 'json' and field is not None:
+        if self.filetype == "json" and field is not None:
             dataset_dict = load_dataset(self.filetype, data_files={stage: str(file)}, field=field)
         else:
             dataset_dict = load_dataset(self.filetype, data_files={stage: str(file)})
 
         dataset = dataset_dict[stage]
         meta = {"root": os.path.dirname(file)}
-        return [{
-            DefaultDataKeys.INPUT: input_file,
-            DefaultDataKeys.TARGET: target,
-            DefaultDataKeys.METADATA: meta,
-        } for input_file, target in zip(dataset[input_key], dataset[target_key])]
+        return [
+            {
+                DefaultDataKeys.INPUT: input_file,
+                DefaultDataKeys.TARGET: target,
+                DefaultDataKeys.METADATA: meta,
+            }
+            for input_file, target in zip(dataset[input_key], dataset[target_key])
+        ]
 
     def load_sample(self, sample: Dict[str, Any], dataset: Any = None) -> Any:
         return self._load_sample(sample)
 
 
 class SpeechRecognitionCSVDataSource(SpeechRecognitionFileDataSource):
-
     def __init__(self):
-        super().__init__(filetype='csv')
+        super().__init__(filetype="csv")
 
 
 class SpeechRecognitionJSONDataSource(SpeechRecognitionFileDataSource):
-
     def __init__(self):
-        super().__init__(filetype='json')
+        super().__init__(filetype="json")
 
 
 class SpeechRecognitionDatasetDataSource(DatasetDataSource, BaseSpeechRecognition):
-
     def load_data(self, data: Dataset, dataset: Optional[Any] = None) -> Union[Sequence[Mapping[str, Any]]]:
         if isinstance(data, HFDataset):
             data = list(zip(data["file"], data["text"]))
         return super().load_data(data, dataset)
 
 
 class SpeechRecognitionPathsDataSource(PathsDataSource, BaseSpeechRecognition):
-
     def __init__(self):
         super().__init__(("wav", "ogg", "flac", "mat"))
 
@@ -139,7 +136,6 @@ def load_sample(self, sample: Dict[str, Any], dataset: Any = None) -> Any:
 
 
 class SpeechRecognitionPreprocess(Preprocess):
-
     @requires_extras("audio")
     def __init__(
         self,
@@ -181,7 +177,6 @@ class SpeechRecognitionBackboneState(ProcessState):
 
 
 class SpeechRecognitionPostprocess(Postprocess):
-
     @requires_extras("audio")
     def __init__(self):
         super().__init__()

flash/audio/speech_recognition/model.py

@@ -51,8 +51,9 @@ def __init__(
         # set os environ variable for multiprocesses
         os.environ["PYTHONWARNINGS"] = "ignore"
 
-        model = self.backbones.get(backbone
-                                   )() if backbone in self.backbones else Wav2Vec2ForCTC.from_pretrained(backbone)
+        model = (
+            self.backbones.get(backbone)() if backbone in self.backbones else Wav2Vec2ForCTC.from_pretrained(backbone)
+        )
         super().__init__(
             model=model,
             loss_fn=loss_fn,
@@ -74,5 +75,5 @@ def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> A
 
     def step(self, batch: Any, batch_idx: int, metrics: nn.ModuleDict) -> Any:
         out = self.model(batch["input_values"], labels=batch["labels"])
-        out["logs"] = {'loss': out.loss}
+        out["logs"] = {"loss": out.loss}
         return out