Onboard segmentation inputs to new object

docs/source/api/image.rst

@@ -97,9 +97,11 @@ ____________
     ~segmentation.data.SemanticSegmentationInputTransform
 
     segmentation.data.SegmentationMatplotlibVisualization
+    segmentation.data.SemanticSegmentationInput
+    segmentation.data.SemanticSegmentationFilesInput
+    segmentation.data.SemanticSegmentationFolderInput
     segmentation.data.SemanticSegmentationNumpyInput
     segmentation.data.SemanticSegmentationTensorInput
-    segmentation.data.SemanticSegmentationPathsInput
     segmentation.data.SemanticSegmentationFiftyOneInput
     segmentation.data.SemanticSegmentationDeserializer
     segmentation.model.SemanticSegmentationOutputTransform

flash/core/data/data_module.py

@@ -110,6 +110,14 @@ def __init__(
         if flash._IS_TESTING and torch.cuda.is_available():
             batch_size = 16
 
+        self._train_ds = train_dataset
+        self._val_ds = val_dataset
+        self._test_ds = test_dataset
+        self._predict_ds = predict_dataset
+
+        if self._train_ds and (val_split is not None and not self._val_ds):
+            self._train_ds, self._val_ds = self._split_train_val(self._train_ds, val_split)
+
         self._input: Input = input
         self._input_tranform: Optional[InputTransform] = input_transform
         self._output_transform: Optional[OutputTransform] = output_transform
@@ -119,14 +127,6 @@ def __init__(
         # TODO: InputTransform can change
         self.data_fetcher.attach_to_input_transform(self.input_transform)
 
-        self._train_ds = train_dataset
-        self._val_ds = val_dataset
-        self._test_ds = test_dataset
-        self._predict_ds = predict_dataset
-
-        if self._train_ds and (val_split is not None and not self._val_ds):
-            self._train_ds, self._val_ds = self._split_train_val(self._train_ds, val_split)
-
         if self._train_ds:
             self.train_dataloader = self._train_dataloader
 

flash/core/data/utilities/paths.py

@@ -14,6 +14,8 @@
 import os
 from typing import Any, Callable, cast, List, Optional, Tuple, TypeVar, Union
 
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
+
 PATH_TYPE = Union[str, bytes, os.PathLike]
 
 T = TypeVar("T")
@@ -132,7 +134,7 @@ def list_valid_files(
 def filter_valid_files(
     files: Union[PATH_TYPE, List[PATH_TYPE]],
     *additional_lists: List[Any],
-    valid_extensions: Optional[Tuple[str, ...]] = None
+    valid_extensions: Optional[Tuple[str, ...]] = None,
 ) -> Union[List[Any], Tuple[List[Any], ...]]:
     """Filter the given list of files and any additional lists to include only the entries that contain a file with
     a valid extension.
@@ -148,6 +150,13 @@ def filter_valid_files(
     if not isinstance(files, List):
         files = [files]
 
+    additional_lists = tuple([a] if not isinstance(a, List) else a for a in additional_lists)
+
+    if not all(len(a) == len(files) for a in additional_lists):
+        raise MisconfigurationException(
+            f"The number of files ({len(files)}) and the number of items in any additional lists must be the same."
+        )
+
     if valid_extensions is None:
         return (files,) + additional_lists
     filtered = list(

flash/image/classification/data.py

@@ -100,7 +100,6 @@ class ImageClassificationTensorInput(ClassificationInput, ImageTensorInput):
     def load_data(self, tensor: Any, targets: Optional[List[Any]] = None) -> List[Dict[str, Any]]:
         if targets is not None:
             self.load_target_metadata(targets)
-
         return to_samples(tensor, targets)
 
     def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
@@ -114,7 +113,6 @@ class ImageClassificationNumpyInput(ClassificationInput, ImageNumpyInput):
     def load_data(self, array: Any, targets: Optional[List[Any]] = None) -> List[Dict[str, Any]]:
         if targets is not None:
             self.load_target_metadata(targets)
-
         return to_samples(array, targets)
 
     def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:

flash/image/data.py

@@ -78,7 +78,7 @@ def example_input(self) -> str:
 class ImageInput(Input):
     @requires("image")
     def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
-        w, h = sample[DataKeys.INPUT].size  # WxH
+        w, h = sample[DataKeys.INPUT].size  # W x H
         if DataKeys.METADATA not in sample:
             sample[DataKeys.METADATA] = {}
         sample[DataKeys.METADATA]["size"] = (h, w)
@@ -99,13 +99,19 @@ def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
 
 
 class ImageTensorInput(ImageInput):
+    def load_data(self, tensor: Any) -> List[Dict[str, Any]]:
+        return to_samples(tensor)
+
     def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
         img = to_pil_image(sample[DataKeys.INPUT])
         sample[DataKeys.INPUT] = img
         return super().load_sample(sample)
 
 
 class ImageNumpyInput(ImageInput):
+    def load_data(self, array: Any) -> List[Dict[str, Any]]:
+        return to_samples(array)
+
     def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
         img = to_pil_image(torch.from_numpy(sample[DataKeys.INPUT]))
         sample[DataKeys.INPUT] = img