Temporary fix for RTD build (#605)

* Try something * Try something * Try something * Try something * Try something * Try something * Try something * Try something * Add few more paths * Test * Drop * Add back, remove requires * Remove * task * temp * test * test * test * ttempt * Format code with autopep8 * attempt * attempt * temp * Format code with autopep8 * Fix a few * Format code with autopep8 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Try fix * Try fix * Try fix * Try something * Try something * Try something * Try something * Cleaning * Fixes * Remove CI addition Co-authored-by: SeanNaren <[email protected]> Co-authored-by: deepsource-autofix[bot] <62050782+deepsource-autofix[bot]@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Lightning-Universe · Jul 23, 2021 · ffe31b5 · ffe31b5
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diff --git a/docs/source/api/audio.rst b/docs/source/api/audio.rst
@@ -28,8 +28,8 @@ __________________
     :nosignatures:
     :template: classtemplate.rst
 
-    ~speech_recognition.model.SpeechRecognition
     ~speech_recognition.data.SpeechRecognitionData
+    ~speech_recognition.model.SpeechRecognition
 
     speech_recognition.data.SpeechRecognitionPreprocess
     speech_recognition.data.SpeechRecognitionBackboneState

diff --git a/docs/source/api/pointcloud.rst b/docs/source/api/pointcloud.rst
@@ -9,22 +9,6 @@ flash.pointcloud
 
 .. currentmodule:: flash.pointcloud
 
-Segmentation
-____________
-
-.. autosummary::
-    :toctree: generated/
-    :nosignatures:
-    :template: classtemplate.rst
-
-    ~segmentation.model.PointCloudSegmentation
-    ~segmentation.data.PointCloudSegmentationData
-
-    segmentation.data.PointCloudSegmentationPreprocess
-    segmentation.data.PointCloudSegmentationFoldersDataSource
-    segmentation.data.PointCloudSegmentationDatasetDataSource
-
-
 Object Detection
 ________________
 

diff --git a/docs/source/reference/pointcloud_segmentation.rst b/docs/source/reference/pointcloud_segmentation.rst
@@ -57,9 +57,9 @@ Here's the structure:
 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.
+Once we've downloaded the data using :func:`~flash.core.data.download_data`, we create the ``PointCloudSegmentationData``.
+We select a pre-trained ``randlanet_semantic_kitti`` backbone for our ``PointCloudSegmentation`` task.
+We then use the trained ``PointCloudSegmentation`` for inference.
 Finally, we save the model.
 Here's the full example:
 

diff --git a/flash/audio/speech_recognition/model.py b/flash/audio/speech_recognition/model.py
@@ -18,12 +18,12 @@
 import torch
 import torch.nn as nn
 
-from flash import Task
 from flash.audio.speech_recognition.backbone import SPEECH_RECOGNITION_BACKBONES
 from flash.audio.speech_recognition.collate import DataCollatorCTCWithPadding
 from flash.audio.speech_recognition.data import SpeechRecognitionBackboneState
 from flash.core.data.process import Serializer
 from flash.core.data.states import CollateFn
+from flash.core.model import Task
 from flash.core.registry import FlashRegistry
 from flash.core.utilities.imports import _AUDIO_AVAILABLE
 

diff --git a/flash/core/utilities/imports.py b/flash/core/utilities/imports.py
@@ -116,7 +116,7 @@ def _compare_version(package: str, op, version) -> bool:
     _SEGMENTATION_MODELS_AVAILABLE,
 ])
 _SERVE_AVAILABLE = _FASTAPI_AVAILABLE and _PYDANTIC_AVAILABLE and _CYTOOLZ_AVAILABLE and _UVICORN_AVAILABLE
-_POINTCLOUD_AVAILABLE = _OPEN3D_AVAILABLE
+_POINTCLOUD_AVAILABLE = _OPEN3D_AVAILABLE and _TORCHVISION_AVAILABLE
 _AUDIO_AVAILABLE = all([_ASTEROID_AVAILABLE, _TORCHAUDIO_AVAILABLE, _SOUNDFILE_AVAILABLE, _TRANSFORMERS_AVAILABLE])
 _GRAPH_AVAILABLE = _TORCH_SCATTER_AVAILABLE and _TORCH_SPARSE_AVAILABLE and _TORCH_GEOMETRIC_AVAILABLE
 

diff --git a/flash/pointcloud/detection/data.py b/flash/pointcloud/detection/data.py
@@ -4,9 +4,8 @@
 
 from flash.core.data.base_viz import BaseDataFetcher
 from flash.core.data.data_module import DataModule
-from flash.core.data.data_pipeline import Deserializer
 from flash.core.data.data_source import BaseDataFormat, DataSource, DefaultDataKeys, DefaultDataSources
-from flash.core.data.process import Preprocess
+from flash.core.data.process import Deserializer, Preprocess
 from flash.core.utilities.imports import _POINTCLOUD_AVAILABLE
 
 if _POINTCLOUD_AVAILABLE:

diff --git a/flash/pointcloud/detection/model.py b/flash/pointcloud/detection/model.py
@@ -20,11 +20,11 @@
 from torch.optim.lr_scheduler import _LRScheduler
 from torch.utils.data import DataLoader, Sampler
 
-import flash
 from flash.core.data.auto_dataset import BaseAutoDataset
 from flash.core.data.data_source import DefaultDataKeys
 from flash.core.data.process import Serializer
 from flash.core.data.states import CollateFn
+from flash.core.model import Task
 from flash.core.registry import FlashRegistry
 from flash.core.utilities.apply_func import get_callable_dict
 from flash.core.utilities.imports import _POINTCLOUD_AVAILABLE
@@ -37,7 +37,7 @@ class PointCloudObjectDetectorSerializer(Serializer):
     pass
 
 
-class PointCloudObjectDetector(flash.Task):
+class PointCloudObjectDetector(Task):
     """The ``PointCloudObjectDetector`` is a :class:`~flash.core.classification.ClassificationTask` that classifies
     pointcloud data.
 

diff --git a/flash/pointcloud/detection/open3d_ml/data_sources.py b/flash/pointcloud/detection/open3d_ml/data_sources.py
@@ -11,8 +11,8 @@
 # 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 os
 from os.path import basename, dirname, exists, isdir, isfile, join
-from posix import listdir
 from typing import Any, Dict, List, Optional, Union
 
 import yaml
@@ -69,7 +69,7 @@ def load_meta(self, root_dir, dataset: Optional[BaseAutoDataset]):
         dataset.color_map = self.meta["color_map"]
 
     def load_data(self, folder: str, dataset: Optional[BaseAutoDataset]):
-        sub_directories = listdir(folder)
+        sub_directories = os.listdir(folder)
         if len(sub_directories) != 3:
             raise MisconfigurationException(
                 f"Using KITTI Format, the {folder} should contains 3 directories "
@@ -84,9 +84,9 @@ def load_data(self, folder: str, dataset: Optional[BaseAutoDataset]):
         labels_dir = join(folder, self.labels_folder_name)
         calibrations_dir = join(folder, self.calibrations_folder_name)
 
-        scan_paths = [join(scans_dir, f) for f in listdir(scans_dir)]
-        label_paths = [join(labels_dir, f) for f in listdir(labels_dir)]
-        calibration_paths = [join(calibrations_dir, f) for f in listdir(calibrations_dir)]
+        scan_paths = [join(scans_dir, f) for f in os.listdir(scans_dir)]
+        label_paths = [join(labels_dir, f) for f in os.listdir(labels_dir)]
+        calibration_paths = [join(calibrations_dir, f) for f in os.listdir(calibrations_dir)]
 
         assert len(scan_paths) == len(label_paths) == len(calibration_paths)
 

diff --git a/flash/pointcloud/segmentation/data.py b/flash/pointcloud/segmentation/data.py
@@ -1,13 +1,10 @@
 from typing import Any, Callable, Dict, Optional, Tuple
 
 from flash.core.data.data_module import DataModule
-from flash.core.data.data_pipeline import Deserializer
 from flash.core.data.data_source import DataSource, DefaultDataKeys, DefaultDataSources
-from flash.core.data.process import Preprocess
-from flash.core.utilities.imports import _POINTCLOUD_AVAILABLE, requires_extras
-
-if _POINTCLOUD_AVAILABLE:
-    from flash.pointcloud.segmentation.open3d_ml.sequences_dataset import SequencesDataset
+from flash.core.data.process import Deserializer, Preprocess
+from flash.core.utilities.imports import requires_extras
+from flash.pointcloud.segmentation.open3d_ml.sequences_dataset import SequencesDataset
 
 
 class PointCloudSegmentationDatasetDataSource(DataSource):

diff --git a/flash/pointcloud/segmentation/model.py b/flash/pointcloud/segmentation/model.py
@@ -23,7 +23,6 @@
 from torch.utils.data import DataLoader, Sampler
 from torchmetrics import IoU
 
-import flash
 from flash.core.classification import ClassificationTask
 from flash.core.data.auto_dataset import BaseAutoDataset
 from flash.core.data.data_source import DefaultDataKeys
@@ -112,6 +111,7 @@ def __init__(
         multi_label: bool = False,
         serializer: Optional[Union[Serializer, Mapping[str, Serializer]]] = PointCloudSegmentationSerializer(),
     ):
+        import flash
         if metrics is None:
             metrics = IoU(num_classes=num_classes)
 

diff --git a/flash/pointcloud/segmentation/open3d_ml/app.py b/flash/pointcloud/segmentation/open3d_ml/app.py
@@ -13,87 +13,94 @@
 # limitations under the License.
 import torch
 
-from flash import DataModule
+from flash.core.data.data_module import DataModule
 from flash.core.data.data_source import DefaultDataKeys
 from flash.core.utilities.imports import _POINTCLOUD_AVAILABLE
 
 if _POINTCLOUD_AVAILABLE:
 
     from open3d._ml3d.torch.dataloaders import TorchDataloader
-    from open3d._ml3d.vis.visualizer import LabelLUT, Visualizer
+    from open3d._ml3d.vis.visualizer import LabelLUT
+    from open3d._ml3d.vis.visualizer import Visualizer as Open3dVisualizer
 
-    class Visualizer(Visualizer):
+else:
 
-        def visualize_dataset(self, dataset, split, indices=None, width=1024, height=768):
-            """Visualize a dataset.
+    Open3dVisualizer = object
 
-            Example:
-                Minimal example for visualizing a dataset::
-                    import open3d.ml.torch as ml3d  # or open3d.ml.tf as ml3d
 
-                    dataset = ml3d.datasets.SemanticKITTI(dataset_path='/path/to/SemanticKITTI/')
-                    vis = ml3d.vis.Visualizer()
-                    vis.visualize_dataset(dataset, 'all', indices=range(100))
+class Visualizer(Open3dVisualizer):
 
-            Args:
-                dataset: The dataset to use for visualization.
-                split: The dataset split to be used, such as 'training'
-                indices: An iterable with a subset of the data points to visualize, such as [0,2,3,4].
-                width: The width of the visualization window.
-                height: The height of the visualization window.
-            """
-            # Setup the labels
+    def visualize_dataset(self, dataset, split, indices=None, width=1024, height=768):
+        """Visualize a dataset.
+
+        Example:
+            Minimal example for visualizing a dataset::
+                import open3d.ml.torch as ml3d  # or open3d.ml.tf as ml3d
+
+                dataset = ml3d.datasets.SemanticKITTI(dataset_path='/path/to/SemanticKITTI/')
+                vis = ml3d.vis.Visualizer()
+                vis.visualize_dataset(dataset, 'all', indices=range(100))
+
+        Args:
+            dataset: The dataset to use for visualization.
+            split: The dataset split to be used, such as 'training'
+            indices: An iterable with a subset of the data points to visualize, such as [0,2,3,4].
+            width: The width of the visualization window.
+            height: The height of the visualization window.
+        """
+        # Setup the labels
+        lut = LabelLUT()
+        color_map = dataset.color_map
+        for id, val in dataset.label_to_names.items():
+            lut.add_label(val, id, color=color_map[id])
+        self.set_lut("labels", lut)
+
+        self._consolidate_bounding_boxes = True
+        self._init_dataset(dataset, split, indices)
+        self._visualize("Open3D - " + dataset.name, width, height)
+
+
+class App:
+
+    def __init__(self, datamodule: DataModule):
+        self.datamodule = datamodule
+        self._enabled = True  # not flash._IS_TESTING
+
+    def get_dataset(self, stage: str = "train"):
+        dataloader = getattr(self.datamodule, f"{stage}_dataloader")()
+        dataset = dataloader.dataset.dataset
+        if isinstance(dataset, TorchDataloader):
+            return dataset.dataset
+        return dataset
+
+    def show_train_dataset(self, indices=None):
+        if self._enabled:
+            dataset = self.get_dataset("train")
+            viz = Visualizer()
+            viz.visualize_dataset(dataset, 'all', indices=indices)
+
+    def show_predictions(self, predictions):
+        if self._enabled:
+            dataset = self.get_dataset("train")
+            color_map = dataset.color_map
+
+            predictions_visualizations = []
+            for pred in predictions:
+                predictions_visualizations.append({
+                    "points": torch.stack(pred[DefaultDataKeys.INPUT]),
+                    "labels": torch.stack(pred[DefaultDataKeys.TARGET]),
+                    "predictions": torch.argmax(torch.stack(pred[DefaultDataKeys.PREDS]), axis=-1) + 1,
+                    "name": pred[DefaultDataKeys.METADATA]["name"],
+                })
+
+            viz = Visualizer()
             lut = LabelLUT()
             color_map = dataset.color_map
             for id, val in dataset.label_to_names.items():
                 lut.add_label(val, id, color=color_map[id])
-            self.set_lut("labels", lut)
-
-            self._consolidate_bounding_boxes = True
-            self._init_dataset(dataset, split, indices)
-            self._visualize("Open3D - " + dataset.name, width, height)
-
-    class App:
-
-        def __init__(self, datamodule: DataModule):
-            self.datamodule = datamodule
-            self._enabled = True  # not flash._IS_TESTING
-
-        def get_dataset(self, stage: str = "train"):
-            dataloader = getattr(self.datamodule, f"{stage}_dataloader")()
-            dataset = dataloader.dataset.dataset
-            if isinstance(dataset, TorchDataloader):
-                return dataset.dataset
-            return dataset
-
-        def show_train_dataset(self, indices=None):
-            if self._enabled:
-                dataset = self.get_dataset("train")
-                viz = Visualizer()
-                viz.visualize_dataset(dataset, 'all', indices=indices)
-
-        def show_predictions(self, predictions):
-            if self._enabled:
-                dataset = self.get_dataset("train")
-                color_map = dataset.color_map
-
-                predictions_visualizations = []
-                for pred in predictions:
-                    predictions_visualizations.append({
-                        "points": torch.stack(pred[DefaultDataKeys.INPUT]),
-                        "labels": torch.stack(pred[DefaultDataKeys.TARGET]),
-                        "predictions": torch.argmax(torch.stack(pred[DefaultDataKeys.PREDS]), axis=-1) + 1,
-                        "name": pred[DefaultDataKeys.METADATA]["name"],
-                    })
-
-                viz = Visualizer()
-                lut = LabelLUT()
-                color_map = dataset.color_map
-                for id, val in dataset.label_to_names.items():
-                    lut.add_label(val, id, color=color_map[id])
-                viz.set_lut("labels", lut)
-                viz.set_lut("predictions", lut)
-                viz.visualize(predictions_visualizations)
+            viz.set_lut("labels", lut)
+            viz.set_lut("predictions", lut)
+            viz.visualize(predictions_visualizations)
 
 
 def launch_app(datamodule: DataModule) -> 'App':