Add semantic segmentation task

.gitignore

@@ -153,3 +153,5 @@ wmt_en_ro
 action_youtube_naudio
 kinetics
 movie_posters
+CameraRGB
+CameraSeg

docs/source/index.rst

@@ -29,7 +29,7 @@ Lightning Flash
    reference/translation
    reference/object_detection
    reference/video_classification
-
+   reference/semantic_segmentation
 
 .. toctree::
    :maxdepth: 1

docs/source/reference/semantic_segmentation.rst

@@ -0,0 +1,151 @@
+
+.. _semantinc_segmentation:
+
+######################
+Semantinc Segmentation
+######################
+
+********
+The task
+********
+Semantic segmentation, or image segmentation, is the task of clustering parts of an image together which belong to the same object class. It is a form of pixel-level prediction because each pixel in an image is classified according to a category
+
+See more: https://paperswithcode.com/task/semantic-segmentation
+
+.. raw:: html
+
+   <p>
+     <a href="https://i2.wp.com/syncedreview.com/wp-content/uploads/2019/12/image-9-1.png" >
+       <img src="https://i2.wp.com/syncedreview.com/wp-content/uploads/2019/12/image-9-1.png"/>
+     </a>
+   </p>
+
+------
+
+*********
+Inference
+*********
+
+The :class:`~flash.vision.SemanticSegmentation` is already pre-trained on a generated dataset from `CARLA <http://carla.org/>`_ simulator.
+
+
+Use the :class:`~flash.vision.SemanticSegmentation` pretrained model for inference on any string sequence using :func:`~flash.vision.SemanticSegmentation.predict`:
+
+.. code-block:: python
+
+    # import our libraries
+    from flash.data.utils import download_data
+    from flash.vision import SemanticSegmentation
+    from flash.vision.segmentation.serialization import SegmentationLabels
+
+    # 1. Download the data
+    download_data(
+        "https://github.com/ongchinkiat/LyftPerceptionChallenge/releases/download/v0.1/carla-capture-20180513A.zip",
+        "data/"
+    )
+
+    # 2. Load the model from a checkpoint
+    model = SemanticSegmentation.load_from_checkpoint(
+        "https://flash-weights.s3.amazonaws.com/semantic_segmentation_model.pt"
+    )
+    model.serializer = SegmentationLabels(visualize=True)
+
+    # 3. Predict what's on a few images and visualize!
+    predictions = model.predict([
+        'data/CameraRGB/F61-1.png',
+        'data/CameraRGB/F62-1.png',
+        'data/CameraRGB/F63-1.png',
+    ])
+
+For more advanced inference options, see :ref:`predictions`.
+
+------
+
+**********
+Finetuning
+**********
+
+you now want to customise your model with new data using the same dataset.
+Once we download the data using :func:`~flash.data.download_data`, all we need is the train data and validation data folders to create the :class:`~flash.vision.SemanticSegmentationData`.
+
+.. note:: the dataset is structured in a way that each sample (an image and its corresponding labels) is stored in separated directories but keeping the same filename.
+
+.. code-block::
+
+    data
+    ├── CameraRGB
+    │   ├── F61-1.png
+    │   ├── F61-2.png
+    │       ...
+    └── CameraSeg
+        ├── F61-1.png
+        ├── F61-2.png
+            ...
+
+
+Now all we need is three lines of code to build to train our task!
+
+.. code-block:: python
+
+    import flash
+    from flash.data.utils import download_data
+    from flash.vision import SemanticSegmentation, SemanticSegmentationData
+    from flash.vision.segmentation.serialization import SegmentationLabels
+
+    # 1. Download the data
+    download_data(
+        "https://github.com/ongchinkiat/LyftPerceptionChallenge/releases/download/v0.1/carla-capture-20180513A.zip",
+        "data/"
+    )
+
+    # 2.1 Load the data
+    datamodule = SemanticSegmentationData.from_folders(
+        train_folder="data/CameraRGB",
+        train_target_folder="data/CameraSeg",
+        batch_size=4,
+        val_split=0.3,
+        image_size=(200, 200),  # (600, 800)
+    )
+
+    # 2.2 Visualise the samples
+    labels_map = SegmentationLabels.create_random_labels_map(num_classes=21)
+    datamodule.set_labels_map(labels_map)
+    datamodule.show_train_batch(["load_sample", "post_tensor_transform"])
+
+    # 3. Build the model
+    model = SemanticSegmentation(backbone="torchvision/fcn_resnet50", num_classes=21)
+
+    # 4. Create the trainer.
+    trainer = flash.Trainer(max_epochs=1)
+
+    # 5. Train the model
+    trainer.finetune(model, datamodule=datamodule, strategy='freeze')
+
+    # 7. Save it!
+    trainer.save_checkpoint("semantic_segmentation_model.pt")
+
+------
+
+*************
+API reference
+*************
+
+.. _segmentation:
+
+SemanticSegmentation
+--------------------
+
+.. autoclass:: flash.vision.SemanticSegmentation
+    :members:
+    :exclude-members: forward
+
+.. _segmentation_data:
+
+SemanticSegmentationData
+------------------------
+
+.. autoclass:: flash.vision.SemanticSegmentationData
+
+.. automethod:: flash.vision.SemanticSegmentationData.from_folders
+
+.. autoclass:: flash.vision.SemanticSegmentationPreprocess

flash/core/classification.py

@@ -55,7 +55,8 @@ def __init__(
     def to_metrics_format(self, x: torch.Tensor) -> torch.Tensor:
         if getattr(self.hparams, "multi_label", False):
             return torch.sigmoid(x)
-        return torch.softmax(x, -1)
+        # we'll assume that the data always comes as `(B, C, ...)`
+        return torch.softmax(x, dim=1)
 
 
 class ClassificationSerializer(Serializer):

flash/data/batch.py

@@ -138,6 +138,12 @@ def __init__(
         self._per_batch_transform_context = CurrentFuncContext(f"per_batch_transform{extension}", preprocess)
 
     def forward(self, samples: Sequence[Any]) -> Any:
+        # we create a new dict to prevent from potential memory leaks
+        # assuming that the dictionary samples are stored in between and
+        # potentially modified before the transforms are applied.
+        if isinstance(samples, dict):
+            samples = dict(samples.items())
+
         with self._current_stage_context:
 
             if self.apply_per_sample_transform:

flash/data/data_module.py

@@ -190,6 +190,8 @@ def _show_batch(self, stage: str, func_names: Union[str, List[str]], reset: bool
                 _ = next(iter_dataloader)
             data_fetcher: BaseVisualization = self.data_fetcher
             data_fetcher._show(stage, func_names)
+            if reset:
+                self.data_fetcher.batches[stage] = {}
 
     def show_train_batch(self, hooks_names: Union[str, List[str]] = 'load_sample', reset: bool = True) -> None:
         """This function is used to visualize a batch from the train dataloader."""

flash/data/transforms.py

@@ -27,15 +27,18 @@ def __init__(self, keys: Union[str, Sequence[str]], *args):
         self.keys = keys
 
     def forward(self, x: Mapping[str, Any]) -> Mapping[str, Any]:
-        inputs = [x[key] for key in filter(lambda key: key in x, self.keys)]
+        keys = list(filter(lambda key: key in x, self.keys))
+        inputs = [x[key] for key in keys]
         if len(inputs) > 0:
-            outputs = super().forward(*inputs)
-            if not isinstance(outputs, tuple):
+            if len(inputs) == 1:
+                inputs = inputs[0]
+            outputs = super().forward(inputs)
+            if not isinstance(outputs, Sequence):
                 outputs = (outputs, )
 
             result = {}
             result.update(x)
-            for i, key in enumerate(self.keys):
+            for i, key in enumerate(keys):
                 result[key] = outputs[i]
             return result
         return x

flash/vision/__init__.py

@@ -2,3 +2,4 @@
 from flash.vision.classification import ImageClassificationData, ImageClassificationPreprocess, ImageClassifier
 from flash.vision.detection import ObjectDetectionData, ObjectDetector
 from flash.vision.embedding import ImageEmbedder
+from flash.vision.segmentation import SemanticSegmentation, SemanticSegmentationData, SemanticSegmentationPreprocess

flash/vision/classification/data.py

@@ -73,7 +73,7 @@ def collate(self, samples: Sequence[Dict[str, Any]]) -> Any:
             for key in sample.keys():
                 if torch.is_tensor(sample[key]):
                     sample[key] = sample[key].squeeze(0)
-        return default_collate(samples)
+        return super().collate(samples)
 
     @property
     def default_train_transforms(self) -> Optional[Dict[str, Callable]]:

flash/vision/segmentation/__init__.py

@@ -0,0 +1,2 @@
+from flash.vision.segmentation.data import SemanticSegmentationData, SemanticSegmentationPreprocess
+from flash.vision.segmentation.model import SemanticSegmentation

flash/vision/segmentation/backbones.py

@@ -0,0 +1,36 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+import torch.nn as nn
+
+from flash.core.registry import FlashRegistry
+from flash.utils.imports import _TORCHVISION_AVAILABLE
+
+if _TORCHVISION_AVAILABLE:
+    import torchvision
+
+SEMANTIC_SEGMENTATION_BACKBONES = FlashRegistry("backbones")
+
+
+@SEMANTIC_SEGMENTATION_BACKBONES(name="torchvision/fcn_resnet50")
+def load_torchvision_fcn_resnet50(num_classes: int, pretrained: bool = True) -> nn.Module:
+    model = torchvision.models.segmentation.fcn_resnet50(pretrained=pretrained)
+    model.classifier[-1] = nn.Conv2d(512, num_classes, kernel_size=(1, 1), stride=(1, 1))
+    return model
+
+
+@SEMANTIC_SEGMENTATION_BACKBONES(name="torchvision/fcn_resnet101")
+def load_torchvision_fcn_resnet101(num_classes: int, pretrained: bool = True) -> nn.Module:
+    model = torchvision.models.segmentation.fcn_resnet101(pretrained=pretrained)
+    model.classifier[-1] = nn.Conv2d(512, num_classes, kernel_size=(1, 1), stride=(1, 1))
+    return model