Add IceVision docs page (#677)

Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

docs/source/api/core.rst

@@ -48,15 +48,26 @@ _____________________
     ~flash.core.finetuning.NoFreeze
     ~flash.core.finetuning.UnfreezeMilestones
 
-flash.core.integration.fiftyone
-_______________________________
+flash.core.integrations.fiftyone
+________________________________
 
 .. autosummary::
     :toctree: generated/
     :nosignatures:
 
     ~flash.core.integrations.fiftyone.utils.visualize
 
+flash.core.integrations.icevision
+_________________________________
+
+.. autosummary::
+    :toctree: generated/
+    :nosignatures:
+
+    ~flash.core.integrations.icevision.transforms.IceVisionTransformAdapter
+    ~flash.core.integrations.icevision.transforms.default_transforms
+    ~flash.core.integrations.icevision.transforms.train_default_transforms
+
 flash.core.model
 ________________
 

docs/source/index.rst

@@ -84,6 +84,7 @@ Lightning Flash
 
    integrations/providers
    integrations/fiftyone
+   integrations/icevision
 
 .. toctree::
    :maxdepth: 1

docs/source/integrations/icevision.rst

@@ -0,0 +1,44 @@
+.. _ice_vision:
+
+#########
+IceVision
+#########
+
+IceVision from airctic is an awesome computer vision framework which offers a curated collection of hundreds of high-quality pre-trained models for: object detection, keypoint detection, and instance segmentation.
+In Flash, we've integrated the IceVision framework to provide: data loading, augmentation, backbones, and heads.
+We use IceVision components in our: :ref:`object detection <object_detection>`, :ref:`instance segmentation <instance_segmentation>`, and :ref:`keypoint detection <keypoint_detection>` tasks.
+Take a look at `their documentation <https://airctic.com/>`_ and star `IceVision on GitHub <https://github.com/airctic/IceVision>`_ to spread the open source love!
+
+IceData
+_______
+
+The `IceData library <https://github.com/airctic/icedata>`_ is a community driven dataset hub for IceVision.
+All of the datasets in IceData can be used out of the box with flash using our ``.from_folders`` methods and the ``parser`` argument.
+Take a look at our :ref:`keypoint_detection` page for an example.
+
+Albumentations with IceVision and Flash
+_______________________________________
+
+IceVision provides two utilities for using the `albumentations library <https://albumentations.ai/>`_ with their models:
+- the ``Adapter`` helper class for adapting an any albumentations transform to work with IceVision records,
+- the ``aug_tfms`` utility function that returns a standard augmentation recipe to get the most out of your model.
+
+In Flash, we use the ``aug_tfms`` as default transforms for the: :ref:`object detection <object_detection>`, :ref:`instance segmentation <instance_segmentation>`, and :ref:`keypoint detection <keypoint_detection>` tasks.
+You can also provide custom transforms from albumentations using the :class:`~flash.core.integrations.icevision.transforms.IceVisionTransformAdapter` (which relies on the IceVision ``Adapter`` underneath).
+Here's an example:
+
+.. code-block:: python
+
+    import albumentations as A
+
+    from flash.core.integrations.icevision.transforms import IceVisionTransformAdapter
+    from flash.image import ObjectDetectionData
+
+    train_transform = {
+        "pre_tensor_transform": IceVisionTransformAdapter([A.HorizontalFlip(), A.Normalize()]),
+    }
+
+    datamodule = ObjectDetectionData.from_coco(
+        ...,
+        train_transform=train_transform,
+    )

flash/core/integrations/icevision/transforms.py

@@ -174,7 +174,7 @@ def from_icevision_record(record: "BaseRecord"):
 class IceVisionTransformAdapter(nn.Module):
     def __init__(self, transform):
         super().__init__()
-        self.transform = transform
+        self.transform = A.Adapter(transform)
 
     def forward(self, x):
         record = to_icevision_record(x)
@@ -186,13 +186,13 @@ def forward(self, x):
 def default_transforms(image_size: Tuple[int, int]) -> Dict[str, Callable]:
     """The default transforms from IceVision."""
     return {
-        "pre_tensor_transform": IceVisionTransformAdapter(A.Adapter([*A.resize_and_pad(image_size), A.Normalize()])),
+        "pre_tensor_transform": IceVisionTransformAdapter([*A.resize_and_pad(image_size), A.Normalize()]),
     }
 
 
 @requires_extras("image")
 def train_default_transforms(image_size: Tuple[int, int]) -> Dict[str, Callable]:
     """The default augmentations from IceVision."""
     return {
-        "pre_tensor_transform": IceVisionTransformAdapter(A.Adapter([*A.aug_tfms(size=image_size), A.Normalize()])),
+        "pre_tensor_transform": IceVisionTransformAdapter([*A.aug_tfms(size=image_size), A.Normalize()]),
     }