[Feat] Add PointCloud Segmentation

.github/workflows/ci-testing.yml

@@ -49,6 +49,10 @@ jobs:
             python-version: 3.8
             requires: 'latest'
             topic: ['text']
+          - os: ubuntu-20.04
+            python-version: 3.8
+            requires: 'latest'
+            topic: ['pointcloud']
           - os: ubuntu-20.04
             python-version: 3.8
             requires: 'latest'

.gitignore

@@ -159,3 +159,4 @@ CameraRGB
 CameraSeg
 jigsaw_toxic_comments
 flash_examples/serve/tabular_classification/data
+logs/cache/*

CHANGELOG.md

@@ -18,6 +18,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added support for nesting of `Task` objects ([#575](https://github.com/PyTorchLightning/lightning-flash/pull/575))
 
+- Added `PointCloudSegmentation` Task ([#566](https://github.com/PyTorchLightning/lightning-flash/pull/566))
+
+
 ### Changed
 
 - Changed how pretrained flag works for loading weights for ImageClassifier task ([#560](https://github.com/PyTorchLightning/lightning-flash/pull/560))

README.md

@@ -605,7 +605,7 @@ For help or questions, join our huge community on [Slack](https://join.slack.com
 ## Citations
 We’re excited to continue the strong legacy of opensource software and have been inspired over the years by Caffee, Theano, Keras, PyTorch, torchbearer, and fast.ai. When/if a paper is written about this, we’ll be happy to cite these frameworks and the corresponding authors.
 
-Flash leverages models from [torchvision](https://pytorch.org/vision/stable/index.html), [huggingface/transformers](https://huggingface.co/transformers/), [timm](https://github.com/rwightman/pytorch-image-models), [pytorch-tabnet](https://dreamquark-ai.github.io/tabnet/), and [asteroid](https://github.com/asteroid-team/asteroid) for the `vision`, `text`, `tabular`, and `audio` tasks respectively. Also supports self-supervised backbones from [bolts](https://github.com/PyTorchLightning/lightning-bolts).
+Flash leverages models from [torchvision](https://pytorch.org/vision/stable/index.html), [huggingface/transformers](https://huggingface.co/transformers/), [timm](https://github.com/rwightman/pytorch-image-models), [open3d-ml](https://github.com/intel-isl/Open3D-ML) for pointcloud, [pytorch-tabnet](https://dreamquark-ai.github.io/tabnet/), and [asteroid](https://github.com/asteroid-team/asteroid) for the `vision`, `text`, `tabular`, and `audio` tasks respectively. Also supports self-supervised backbones from [bolts](https://github.com/PyTorchLightning/lightning-bolts).
 
 ## License
 Please observe the Apache 2.0 license that is listed in this repository. In addition

docs/source/api/pointcloud.rst

@@ -0,0 +1,24 @@
+################
+flash.pointcloud
+################
+
+.. contents::
+    :depth: 1
+    :local:
+    :backlinks: top
+
+.. currentmodule:: flash.pointcloud
+
+Segmentation
+____________
+
+.. autosummary::
+    :toctree: generated/
+    :nosignatures:
+    :template: classtemplate.rst
+
+    ~segmentation.data.PointCloudSegmentationData
+    ~segmentation.data.PointCloudSegmentationPreprocess
+    ~segmentation.data.PointCloudSegmentationFoldersDataSource
+    ~segmentation.data.PointCloudSegmentationDatasetDataSource
+    ~segmentation.model.PointCloudSegmentation

docs/source/index.rst

@@ -55,6 +55,12 @@ Lightning Flash
    reference/summarization
    reference/translation
 
+.. toctree::
+   :maxdepth: 1
+   :caption: PointCloud
+
+   reference/pointcloud_segmentation
+
 .. toctree::
    :maxdepth: 1
    :caption: Integrations
@@ -70,6 +76,7 @@ Lightning Flash
    api/data
    api/serve
    api/image
+   api/pointcloud
    api/tabular
    api/text
    api/video

docs/source/reference/pointcloud_segmentation.rst

@@ -0,0 +1,73 @@
+
+.. _pointcloud_segmentation:
+
+#######################
+PointCloud Segmentation
+#######################
+
+********
+The Task
+********
+
+A Point Cloud is a set of data points in space, usually describes by ``x``, ``y`` and ``z`` coordinates.
+
+PointCloud Segmentation is the task of performing classification at a point-level, meaning each point will associated to a given class.
+The current integration builds on top `Open3D-ML <https://github.com/intel-isl/Open3D-ML>`_.
+
+------
+
+*******
+Example
+*******
+
+Let's look at an example using a data set generated from the `KITTI Vision Benchmark  <http://www.semantic-kitti.org/dataset.html>`_.
+The data are a tiny subset of the original dataset and contains sequences of point clouds.
+The data contains multiple folder, one for each sequence and a meta.yaml file describing the classes and their official associated color map.
+A sequence should contain one folder for scans and one folder for labels, plus a ``pose.txt`` to re-align the sequence if required.
+Here's the structure:
+
+.. code-block::
+
+    data
+    ├── meta.yaml
+    ├── 00
+    │   ├── scans
+    |   |    ├── 00000.bin
+    |   |    ├── 00001.bin
+    |   |    ...
+    │   ├── labels
+    |   |    ├── 00000.label
+    |   |    ├── 00001.label
+    |   |   ...
+    |   ├── pose.txt
+    │   ...
+    |
+    └── XX
+       ├── scans
+       |    ├── 00000.bin
+       |    ├── 00001.bin
+       |    ...
+       ├── labels
+       |    ├── 00000.label
+       |    ├── 00001.label
+       |   ...
+       ├── pose.txt
+
+
+Learn more: http://www.semantic-kitti.org/dataset.html
+
+
+Once we've downloaded the data using :func:`~flash.core.data.download_data`, we create the :class:`~flash.image.segmentation.data.PointCloudSegmentationData`.
+We select a pre-trained ``randlanet_semantic_kitti`` backbone for our :class:`~flash.image.segmentation.model.PointCloudSegmentation` task.
+We then use the trained :class:`~flash.image.segmentation.model.PointCloudSegmentation` for inference.
+Finally, we save the model.
+Here's the full example:
+
+.. literalinclude:: ../../../flash_examples/pointcloud_segmentation.py
+    :language: python
+    :lines: 14-
+
+
+
+.. image:: https://raw.githubusercontent.com/intel-isl/Open3D-ML/master/docs/images/getting_started_ml_visualizer.gif
+   :width: 100%

docs/source/reference/semantic_segmentation.rst

@@ -34,9 +34,9 @@ Here's the structure:
         ├── F61-2.png
             ...
 
-Once we've downloaded the data using :func:`~flash.core.data.download_data`, we create the :class:`~flash.image.segmentation.data.SemanticSegmentationData`.
-We select a pre-trained ``mobilenet_v3_large`` backbone with an ``fpn`` head to use for our :class:`~flash.image.segmentation.model.SemanticSegmentation` task and fine-tune on the CARLA data.
-We then use the trained :class:`~flash.image.segmentation.model.SemanticSegmentation` for inference.
+Once we've downloaded the data using :func:`~flash.core.data.download_data`, we create the :class:`~flash.image.segmentation.data.PointCloudSegmentationData`.
+We select a pre-trained ``randlanet_PointCloud_kitti`` backbone for our :class:`~flash.image.segmentation.model.PointCloudSegmentation` task and fine-tune on the CARLA data.
+We then use the trained :class:`~flash.image.segmentation.model.PointCloudSegmentation` for inference.
 Finally, we save the model.
 Here's the full example:
 

flash/core/data/batch.py

@@ -290,7 +290,7 @@ def __init__(
     @staticmethod
     def _extract_metadata(batch: Any) -> Tuple[Any, Optional[Any]]:
         if isinstance(batch, Mapping):
-            return batch, batch.get(DefaultDataKeys.METADATA, None)
+            return batch, batch.pop(DefaultDataKeys.METADATA, None)
         return batch, None
 
     def forward(self, batch: Sequence[Any]):
@@ -331,7 +331,6 @@ def __str__(self) -> str:
 def default_uncollate(batch: Any):
     """
     This function is used to uncollate a batch into samples.
-
     Examples:
         >>> a, b = default_uncollate(torch.rand((2,1)))
     """

flash/core/data/data_module.py

@@ -275,37 +275,79 @@ def _resolve_collate_fn(self, dataset: Dataset, running_stage: RunningStage) ->
     def _train_dataloader(self) -> DataLoader:
         train_ds: Dataset = self._train_ds() if isinstance(self._train_ds, Callable) else self._train_ds
         shuffle: bool = False
+        collate_fn = self._resolve_collate_fn(train_ds, RunningStage.TRAINING)
+        sampler = self.sampler
+        drop_last = False
+        pin_memory = True
+
         if self.sampler is None:
             shuffle = not isinstance(train_ds, (IterableDataset, IterableAutoDataset))
+
+        if isinstance(getattr(self, "trainer", None), pl.Trainer):
+            return self.trainer.lightning_module.process_train_dataset(
+                train_ds,
+                batch_size=self.batch_size,
+                num_workers=self.num_workers,
+                pin_memory=pin_memory,
+                shuffle=shuffle,
+                drop_last=drop_last,
+                collate_fn=collate_fn,
+                sampler=sampler
+            )
+
         return DataLoader(
             train_ds,
             batch_size=self.batch_size,
             shuffle=shuffle,
-            sampler=self.sampler,
+            sampler=sampler,
             num_workers=self.num_workers,
-            pin_memory=True,
-            drop_last=True,
-            collate_fn=self._resolve_collate_fn(train_ds, RunningStage.TRAINING)
+            pin_memory=pin_memory,
+            drop_last=drop_last,
+            collate_fn=collate_fn
         )
 
     def _val_dataloader(self) -> DataLoader:
         val_ds: Dataset = self._val_ds() if isinstance(self._val_ds, Callable) else self._val_ds
+        collate_fn = self._resolve_collate_fn(val_ds, RunningStage.VALIDATING)
+        pin_memory = True
+
+        if isinstance(getattr(self, "trainer", None), pl.Trainer):
+            return self.trainer.lightning_module.process_val_dataset(
+                val_ds,
+                batch_size=self.batch_size,
+                num_workers=self.num_workers,
+                pin_memory=pin_memory,
+                collate_fn=collate_fn
+            )
+
         return DataLoader(
             val_ds,
             batch_size=self.batch_size,
             num_workers=self.num_workers,
-            pin_memory=True,
-            collate_fn=self._resolve_collate_fn(val_ds, RunningStage.VALIDATING)
+            pin_memory=pin_memory,
+            collate_fn=collate_fn
         )
 
     def _test_dataloader(self) -> DataLoader:
         test_ds: Dataset = self._test_ds() if isinstance(self._test_ds, Callable) else self._test_ds
+        collate_fn = self._resolve_collate_fn(test_ds, RunningStage.TESTING)
+        pin_memory = True
+
+        if isinstance(getattr(self, "trainer", None), pl.Trainer):
+            return self.trainer.lightning_module.process_test_dataset(
+                test_ds,
+                batch_size=self.batch_size,
+                num_workers=self.num_workers,
+                pin_memory=pin_memory,
+                collate_fn=collate_fn
+            )
+
         return DataLoader(
             test_ds,
             batch_size=self.batch_size,
             num_workers=self.num_workers,
-            pin_memory=True,
-            collate_fn=self._resolve_collate_fn(test_ds, RunningStage.TESTING)
+            pin_memory=pin_memory,
+            collate_fn=collate_fn
         )
 
     def _predict_dataloader(self) -> DataLoader:
@@ -314,12 +356,21 @@ def _predict_dataloader(self) -> DataLoader:
             batch_size = self.batch_size
         else:
             batch_size = min(self.batch_size, len(predict_ds) if len(predict_ds) > 0 else 1)
+
+        collate_fn = self._resolve_collate_fn(predict_ds, RunningStage.PREDICTING)
+        pin_memory = True
+
+        if isinstance(getattr(self, "trainer", None), pl.Trainer):
+            return self.trainer.lightning_module.process_test_dataset(
+                predict_ds,
+                batch_size=batch_size,
+                num_workers=self.num_workers,
+                pin_memory=pin_memory,
+                collate_fn=collate_fn
+            )
+
         return DataLoader(
-            predict_ds,
-            batch_size=batch_size,
-            num_workers=self.num_workers,
-            pin_memory=True,
-            collate_fn=self._resolve_collate_fn(predict_ds, RunningStage.PREDICTING)
+            predict_ds, batch_size=batch_size, num_workers=self.num_workers, pin_memory=True, collate_fn=collate_fn
         )
 
     @property

flash/core/data/process.py

@@ -26,6 +26,7 @@
 from flash.core.data.callback import FlashCallback
 from flash.core.data.data_source import DatasetDataSource, DataSource, DefaultDataSources
 from flash.core.data.properties import Properties
+from flash.core.data.states import CollateFn
 from flash.core.data.utils import _PREPROCESS_FUNCS, _STAGES_PREFIX, convert_to_modules, CurrentRunningStageFuncContext
 
 
@@ -361,6 +362,11 @@ def per_batch_transform(self, batch: Any) -> Any:
 
     def collate(self, samples: Sequence) -> Any:
         """ Transform to convert a sequence of samples to a collated batch. """
+
+        collate_fn = self.get_state(CollateFn)
+        if collate_fn is not None:
+            return collate_fn.collate_fn(samples)
+
         current_transform = self.current_transform
         if current_transform is self._identity:
             return self._default_collate(samples)

flash/core/data/states.py

@@ -0,0 +1,10 @@
+from dataclasses import dataclass
+from typing import Callable, Optional
+
+from flash.core.data.properties import ProcessState
+
+
+@dataclass(unsafe_hash=True, frozen=True)
+class CollateFn(ProcessState):
+
+    collate_fn: Optional[Callable] = None