precommit

docs/extensions/autodatasources.py

@@ -61,10 +61,7 @@ def _resolve_transforms(_):
                 return None
 
         input_transform = PatchedInputTransform()
-        inputs = {
-            : input_transform._of_name()
-            for  in input_transform.available_inputs()
-        }
+        inputs = {input: input_transform.input_of_name(input) for input in input_transform.available_inputs()}
 
         ENVIRONMENT.get_template("base.rst")
 

docs/source/general/data.rst

@@ -307,15 +307,15 @@ Here is the :class:`~flash.core.data.auto_dataset.AutoDataset` pseudo-code.
         def __init__(
             self,
             data: List[Any],  # output of `Input.load_data`
-            : Input,
+            input: Input,
             running_stage: RunningStage,
         ):
 
             self.data = data
-            self. = 
+            self.input = input
 
         def __getitem__(self, index: int):
-            return self..load_sample(self.data[index])
+            return self.input.load_sample(self.data[index])
 
         def __len__(self):
             return len(self.data)

flash/__init__.py

@@ -22,6 +22,7 @@
     from flash.core.data.callback import FlashCallback
     from flash.core.data.data_module import DataModule  # noqa: E402
     from flash.core.data.datasets import FlashDataset, FlashIterableDataset
+    from flash.core.data.io.input import Input
     from flash.core.data.io.input_transform import InputTransform
     from flash.core.data.io.output import Output
     from flash.core.data.io.output_transform import OutputTransform

flash/audio/speech_recognition/data.py

@@ -23,13 +23,7 @@
 
 import flash
 from flash.core.data.data_module import DataModule
-from flash.core.data.io.input import (
-    DatasetInput,
-    Input,
-    InputDataKeys,
-    InputFormat,
-    PathsInput,
-)
+from flash.core.data.io.input import DatasetInput, Input, InputDataKeys, InputFormat, PathsInput
 from flash.core.data.io.input_transform import InputTransform
 from flash.core.data.io.output_transform import OutputTransform
 from flash.core.data.process import Deserializer

flash/core/data/batch.py

@@ -11,16 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Callable, Sequence, TYPE_CHECKING
+from typing import Callable, TYPE_CHECKING
 
 import torch
 
 from flash.core.data.callback import ControlFlow
-from flash.core.data.utils import (
-    convert_to_modules,
-    CurrentFuncContext,
-    CurrentRunningStageContext,
-)
+from flash.core.data.utils import convert_to_modules, CurrentFuncContext, CurrentRunningStageContext
 from flash.core.utilities.stages import RunningStage
 
 if TYPE_CHECKING:

flash/core/data/io/input_transform.py

@@ -21,7 +21,7 @@
 from torch.utils.data._utils.collate import default_collate
 
 from flash.core.data.callback import ControlFlow, FlashCallback
-from flash.core.data.io.input import Input, InputFormat, DatasetInput
+from flash.core.data.io.input import DatasetInput, Input, InputDataKeys, InputFormat
 from flash.core.data.process import Deserializer
 from flash.core.data.properties import ProcessState, Properties
 from flash.core.data.states import (
@@ -268,9 +268,9 @@ def _check_transforms(
             return transform
 
         if isinstance(transform, list):
-            transform = {"pre_tensor_transform": ApplyToKeys(DefaultDataKeys.INPUT, torch.nn.Sequential(*transform))}
+            transform = {"pre_tensor_transform": ApplyToKeys(InputDataKeys.INPUT, torch.nn.Sequential(*transform))}
         elif callable(transform):
-            transform = {"pre_tensor_transform": ApplyToKeys(DefaultDataKeys.INPUT, transform)}
+            transform = {"pre_tensor_transform": ApplyToKeys(InputDataKeys.INPUT, transform)}
 
         if not isinstance(transform, Dict):
             raise MisconfigurationException(
@@ -444,7 +444,7 @@ def collate(self, samples: Sequence, metadata=None) -> Any:
             # return collate_fn.collate_fn(samples)
 
         parameters = inspect.signature(collate_fn).parameters
-        if len(parameters) > 1 and DefaultDataKeys.METADATA in parameters:
+        if len(parameters) > 1 and InputDataKeys.METADATA in parameters:
             return collate_fn(samples, metadata)
         return collate_fn(samples)
 
@@ -655,7 +655,7 @@ def __init__(
     def _extract_metadata(
         samples: List[Dict[str, Any]],
     ) -> Tuple[List[Dict[str, Any]], Optional[List[Dict[str, Any]]]]:
-        metadata = [s.pop(DefaultDataKeys.METADATA, None) if isinstance(s, Mapping) else None for s in samples]
+        metadata = [s.pop(InputDataKeys.METADATA, None) if isinstance(s, Mapping) else None for s in samples]
         return samples, metadata if any(m is not None for m in metadata) else None
 
     def forward(self, samples: Sequence[Any]) -> Any:
@@ -689,7 +689,7 @@ def forward(self, samples: Sequence[Any]) -> Any:
                     except TypeError:
                         samples = self.collate_fn(samples)
                     if metadata and isinstance(samples, dict):
-                        samples[DefaultDataKeys.METADATA] = metadata
+                        samples[InputDataKeys.METADATA] = metadata
                     self.callback.on_collate(samples, self.stage)
 
             with self._per_batch_transform_context:

flash/core/integrations/labelstudio/input.py

@@ -7,7 +7,7 @@
 from pytorch_lightning.utilities.cloud_io import get_filesystem
 
 from flash.core.data.auto_dataset import AutoDataset, IterableAutoDataset
-from flash.core.data.io.input import Input, InputDataKeys, has_len
+from flash.core.data.io.input import has_len, Input, InputDataKeys
 from flash.core.data.utils import image_default_loader
 from flash.core.utilities.imports import _PYTORCHVIDEO_AVAILABLE, _TEXT_AVAILABLE
 from flash.core.utilities.stages import RunningStage

flash/core/utilities/flash_cli.py

@@ -186,9 +186,7 @@ def add_arguments_to_parser(self, parser) -> None:
             if isinstance(input, InputFormat):
                 input = input.value
             if hasattr(self.local_datamodule_class, f"from_{input}"):
-                self.add_subcommand_from_function(
-                    subcommands, getattr(self.local_datamodule_class, f"from_{input}")
-                )
+                self.add_subcommand_from_function(subcommands, getattr(self.local_datamodule_class, f"from_{input}"))
 
         for datamodule_builder in self.additional_datamodule_builders:
             self.add_subcommand_from_function(subcommands, datamodule_builder)

flash/image/detection/data.py

@@ -15,7 +15,7 @@
 
 from flash.core.data.callback import BaseDataFetcher
 from flash.core.data.data_module import DataModule
-from flash.core.data.io.input import InputDataKeys, InputFormat, FiftyOneInput
+from flash.core.data.io.input import FiftyOneInput, InputDataKeys, InputFormat
 from flash.core.data.io.input_transform import InputTransform
 from flash.core.integrations.icevision.data import IceVisionParserInput, IceVisionPathsInput
 from flash.core.integrations.icevision.transforms import default_transforms

flash/image/segmentation/output.py

@@ -17,7 +17,7 @@
 import torch
 
 import flash
-from flash.core.data.io.input import InputDataKeys, ImageLabelsMap
+from flash.core.data.io.input import ImageLabelsMap, InputDataKeys
 from flash.core.data.io.output import Output
 from flash.core.utilities.imports import (
     _FIFTYONE_AVAILABLE,

flash/tabular/regression/model.py

@@ -16,7 +16,7 @@
 import torch
 from torch.nn import functional as F
 
-from flash.core.data. import DefaultDataKeys
+from flash.core.data.io.input import InputDataKeys
 from flash.core.regression import RegressionTask
 from flash.core.utilities.imports import _TABULAR_AVAILABLE
 from flash.core.utilities.types import LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE, OUTPUT_TYPE
@@ -90,19 +90,19 @@ def forward(self, x_in) -> torch.Tensor:
         return self.model(x)[0].flatten()
 
     def training_step(self, batch: Any, batch_idx: int) -> Any:
-        batch = (batch[DefaultDataKeys.INPUT], batch[DefaultDataKeys.TARGET])
+        batch = (batch[InputDataKeys.INPUT], batch[InputDataKeys.TARGET])
         return super().training_step(batch, batch_idx)
 
     def validation_step(self, batch: Any, batch_idx: int) -> Any:
-        batch = (batch[DefaultDataKeys.INPUT], batch[DefaultDataKeys.TARGET])
+        batch = (batch[InputDataKeys.INPUT], batch[InputDataKeys.TARGET])
         return super().validation_step(batch, batch_idx)
 
     def test_step(self, batch: Any, batch_idx: int) -> Any:
-        batch = (batch[DefaultDataKeys.INPUT], batch[DefaultDataKeys.TARGET])
+        batch = (batch[InputDataKeys.INPUT], batch[InputDataKeys.TARGET])
         return super().test_step(batch, batch_idx)
 
     def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any:
-        batch = batch[DefaultDataKeys.INPUT]
+        batch = batch[InputDataKeys.INPUT]
         return self(batch)
 
     @classmethod

flash/text/seq2seq/core/model.py

@@ -151,7 +151,7 @@ def _initialize_model_specific_parameters(self):
 
     @property
     def tokenizer(self) -> "PreTrainedTokenizerBase":
-        return self.data_pipeline..tokenizer
+        return self.data_pipeline.input.tokenizer
 
     def tokenize_labels(self, labels: Tensor) -> List[str]:
         label_str = self.tokenizer.batch_decode(labels, skip_special_tokens=True)

flash/video/classification/data.py

@@ -20,13 +20,7 @@
 from torch.utils.data import Sampler
 
 from flash.core.data.data_module import DataModule
-from flash.core.data.io.input import (
-    InputDataKeys,
-    InputFormat,
-    FiftyOneInput,
-    LabelsState,
-    PathsInput,
-)
+from flash.core.data.io.input import FiftyOneInput, InputDataKeys, InputFormat, LabelsState, PathsInput
 from flash.core.data.io.input_transform import InputTransform
 from flash.core.integrations.labelstudio.input import LabelStudioVideoClassificationInput
 from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _KORNIA_AVAILABLE, _PYTORCHVIDEO_AVAILABLE, lazy_import

flash_examples/flash_components/custom_data_loading.py

@@ -22,8 +22,8 @@
 from torch.utils.data._utils.collate import default_collate
 
 from flash import _PACKAGE_ROOT, FlashDataset
-from flash.core.data.io.input import InputDataKeys
 from flash.core.data.input_transform import INPUT_TRANSFORM_TYPE, InputTransform
+from flash.core.data.io.input import InputDataKeys
 from flash.core.data.new_data_module import DataModule
 from flash.core.data.transforms import ApplyToKeys
 from flash.core.data.utils import download_data

tests/graph/classification/test_model.py

@@ -74,7 +74,7 @@ def test_predict_dataset(tmpdir):
     tudataset = datasets.TUDataset(root=tmpdir, name="KKI")
     model = GraphClassifier(num_features=tudataset.num_features, num_classes=tudataset.num_classes)
     data_pipe = DataPipeline(input_transform=GraphClassificationInputTransform())
-    out = model.predict(tudataset, ="datasets", data_pipeline=data_pipe)
+    out = model.predict(tudataset, input="datasets", data_pipeline=data_pipe)
     assert isinstance(out[0], int)
 
 

tests/image/segmentation/test_model.py

@@ -107,7 +107,7 @@ def test_predict_tensor():
     img = torch.rand(1, 3, 64, 64)
     model = SemanticSegmentation(2, backbone="mobilenetv3_large_100")
     data_pipe = DataPipeline(input_transform=SemanticSegmentationInputTransform(num_classes=1))
-    out = model.predict(img, ="tensors", data_pipeline=data_pipe)
+    out = model.predict(img, input="tensors", data_pipeline=data_pipe)
     assert isinstance(out[0], list)
     assert len(out[0]) == 64
     assert len(out[0][0]) == 64
@@ -118,7 +118,7 @@ def test_predict_numpy():
     img = np.ones((1, 3, 64, 64))
     model = SemanticSegmentation(2, backbone="mobilenetv3_large_100")
     data_pipe = DataPipeline(input_transform=SemanticSegmentationInputTransform(num_classes=1))
-    out = model.predict(img, ="numpy", data_pipeline=data_pipe)
+    out = model.predict(img, input="numpy", data_pipeline=data_pipe)
     assert isinstance(out[0], list)
     assert len(out[0]) == 64
     assert len(out[0][0]) == 64