Merge branch 'master' into feature/768-text-from-data-frame

docs/source/reference/object_detection.rst

@@ -73,3 +73,35 @@ To view configuration options and options for running the object detector with y
 .. code-block:: bash
 
     flash object_detection --help
+
+------
+
+**********************
+Custom Transformations
+**********************
+
+Flash automatically applies some default image / mask transformations and augmentations, but you may wish to customize these for your own use case.
+The base :class:`~flash.core.data.process.Preprocess` defines 7 hooks for different stages in the data loading pipeline.
+For object-detection tasks, you can leverage the transformations from `Albumentations <https://github.com/albumentations-team/albumentations>`__ with the :class:`~flash.core.integrations.icevision.transforms.IceVisionTransformAdapter`.
+
+.. code-block:: python
+
+    import albumentations as alb
+    from icevision.tfms import A
+
+    from flash.core.integrations.icevision.transforms import IceVisionTransformAdapter
+    from flash.image import ObjectDetectionData
+
+    train_transform = {
+        "pre_tensor_transform": transforms.IceVisionTransformAdapter(
+            [*A.resize_and_pad(128), A.Normalize(), A.Flip(0.4), alb.RandomBrightnessContrast()]
+        )
+    }
+
+    datamodule = ObjectDetectionData.from_coco(
+        train_folder="data/coco128/images/train2017/",
+        train_ann_file="data/coco128/annotations/instances_train2017.json",
+        val_split=0.1,
+        image_size=128,
+        train_transform=train_transform,
+    )

flash/core/adapter.py

@@ -110,7 +110,7 @@ def test_epoch_end(self, outputs) -> None:
     def process_train_dataset(
         self,
         dataset: BaseAutoDataset,
-        trainer: flash.Trainer,
+        trainer: "flash.Trainer",
         batch_size: int,
         num_workers: int,
         pin_memory: bool,
@@ -120,13 +120,21 @@ def process_train_dataset(
         sampler: Optional[Sampler] = None,
     ) -> DataLoader:
         return self.adapter.process_train_dataset(
-            dataset, trainer, batch_size, num_workers, pin_memory, collate_fn, shuffle, drop_last, sampler
+            dataset,
+            trainer,
+            batch_size,
+            num_workers,
+            pin_memory,
+            collate_fn,
+            shuffle,
+            drop_last,
+            sampler,
         )
 
     def process_val_dataset(
         self,
         dataset: BaseAutoDataset,
-        trainer: flash.Trainer,
+        trainer: "flash.Trainer",
         batch_size: int,
         num_workers: int,
         pin_memory: bool,
@@ -136,13 +144,21 @@ def process_val_dataset(
         sampler: Optional[Sampler] = None,
     ) -> DataLoader:
         return self.adapter.process_val_dataset(
-            dataset, trainer, batch_size, num_workers, pin_memory, collate_fn, shuffle, drop_last, sampler
+            dataset,
+            trainer,
+            batch_size,
+            num_workers,
+            pin_memory,
+            collate_fn,
+            shuffle,
+            drop_last,
+            sampler,
         )
 
     def process_test_dataset(
         self,
         dataset: BaseAutoDataset,
-        trainer: flash.Trainer,
+        trainer: "flash.Trainer",
         batch_size: int,
         num_workers: int,
         pin_memory: bool,
@@ -152,7 +168,15 @@ def process_test_dataset(
         sampler: Optional[Sampler] = None,
     ) -> DataLoader:
         return self.adapter.process_test_dataset(
-            dataset, trainer, batch_size, num_workers, pin_memory, collate_fn, shuffle, drop_last, sampler
+            dataset,
+            trainer,
+            batch_size,
+            num_workers,
+            pin_memory,
+            collate_fn,
+            shuffle,
+            drop_last,
+            sampler,
         )
 
     def process_predict_dataset(

flash/core/integrations/icevision/transforms.py

@@ -196,7 +196,13 @@ def from_icevision_predictions(predictions: List["Prediction"]):
 
 
 class IceVisionTransformAdapter(nn.Module):
-    def __init__(self, transform):
+    """
+    Args:
+        transform: list of transformation functions to apply
+
+    """
+
+    def __init__(self, transform: List[Callable]):
         super().__init__()
         self.transform = A.Adapter(transform)
 

flash/core/model.py

@@ -144,7 +144,6 @@ def process_train_dataset(
         shuffle: bool = False,
         drop_last: bool = True,
         sampler: Optional[Sampler] = None,
-        persistent_workers: bool = True,
     ) -> DataLoader:
         return self._process_dataset(
             dataset,
@@ -155,7 +154,7 @@ def process_train_dataset(
             shuffle=shuffle,
             drop_last=drop_last,
             sampler=sampler,
-            persistent_workers=persistent_workers and num_workers > 0,
+            persistent_workers=num_workers > 0,
         )
 
     def process_val_dataset(
@@ -169,7 +168,6 @@ def process_val_dataset(
         shuffle: bool = False,
         drop_last: bool = False,
         sampler: Optional[Sampler] = None,
-        persistent_workers: bool = True,
     ) -> DataLoader:
         return self._process_dataset(
             dataset,
@@ -180,7 +178,7 @@ def process_val_dataset(
             shuffle=shuffle,
             drop_last=drop_last,
             sampler=sampler,
-            persistent_workers=persistent_workers and num_workers > 0,
+            persistent_workers=num_workers > 0,
         )
 
     def process_test_dataset(
@@ -194,7 +192,6 @@ def process_test_dataset(
         shuffle: bool = False,
         drop_last: bool = False,
         sampler: Optional[Sampler] = None,
-        persistent_workers: bool = True,
     ) -> DataLoader:
         return self._process_dataset(
             dataset,
@@ -205,7 +202,7 @@ def process_test_dataset(
             shuffle=shuffle,
             drop_last=drop_last,
             sampler=sampler,
-            persistent_workers=persistent_workers and num_workers > 0,
+            persistent_workers=num_workers > 0,
         )
 
     def process_predict_dataset(

flash/core/utilities/imports.py

@@ -17,7 +17,6 @@
 import types
 from importlib.util import find_spec
 from typing import List, Union
-from warnings import warn
 
 from pkg_resources import DistributionNotFound
 
@@ -107,19 +106,8 @@ def _compare_version(package: str, op, version) -> bool:
     from PIL import Image  # noqa: F401
 else:
 
-    class MetaImage(type):
-        def __init__(cls, name, bases, dct):
-            super().__init__(name, bases, dct)
-
-            cls._Image = None
-
-        @property
-        def Image(cls):
-            warn("Mock object called due to missing PIL library. Please use \"pip install 'lightning-flash[image]'\".")
-            return cls._Image
-
-    class Image(metaclass=MetaImage):
-        pass
+    class Image:
+        Image = object
 
 
 if Version:

flash/image/classification/adapters.py

@@ -206,7 +206,7 @@ def _labels_to_indices(data):
 
     def _convert_dataset(
         self,
-        trainer: flash.Trainer,
+        trainer: "flash.Trainer",
         dataset: BaseAutoDataset,
         ways: int,
         shots: int,
@@ -334,14 +334,14 @@ def _sanetize_batch_size(self, batch_size: int) -> int:
     def process_train_dataset(
         self,
         dataset: BaseAutoDataset,
-        trainer: flash.Trainer,
+        trainer: "flash.Trainer",
         batch_size: int,
         num_workers: int,
         pin_memory: bool,
         collate_fn: Callable,
-        shuffle: bool,
-        drop_last: bool,
-        sampler: Optional[Sampler],
+        shuffle: bool = False,
+        drop_last: bool = False,
+        sampler: Optional[Sampler] = None,
     ) -> DataLoader:
         dataset = self._convert_dataset(
             trainer=trainer,
@@ -366,13 +366,12 @@ def process_train_dataset(
             shuffle=shuffle,
             drop_last=drop_last,
             sampler=sampler,
-            persistent_workers=True,
         )
 
     def process_val_dataset(
         self,
         dataset: BaseAutoDataset,
-        trainer: flash.Trainer,
+        trainer: "flash.Trainer",
         batch_size: int,
         num_workers: int,
         pin_memory: bool,
@@ -404,13 +403,12 @@ def process_val_dataset(
             shuffle=shuffle,
             drop_last=drop_last,
             sampler=sampler,
-            persistent_workers=True,
         )
 
     def process_test_dataset(
         self,
         dataset: BaseAutoDataset,
-        trainer: flash.Trainer,
+        trainer: "flash.Trainer",
         batch_size: int,
         num_workers: int,
         pin_memory: bool,
@@ -442,7 +440,6 @@ def process_test_dataset(
             shuffle=shuffle,
             drop_last=drop_last,
             sampler=sampler,
-            persistent_workers=True,
         )
 
     def process_predict_dataset(