[1/N] Data Sources

docs/source/general/data.rst

@@ -21,6 +21,8 @@ Here are common terms you need to be familiar with:
      - The :class:`~flash.data.data_module.DataModule` contains the dataset, transforms and dataloaders.
    * - :class:`~flash.data.data_pipeline.DataPipeline`
      - The :class:`~flash.data.data_pipeline.DataPipeline` is Flash internal object to manage :class:`~flash.data.process.Preprocess` and :class:`~flash.data.process.Postprocess` objects.
+   * - :class:`~flash.data.data_source.DataSource`
+     - The :class:`~flash.data.data_source.DataSource` provides a hook-based API for creating data sets.
    * - :class:`~flash.data.process.Preprocess`
      - The :class:`~flash.data.process.Preprocess` provides a simple hook-based API to encapsulate your pre-processing logic.
         The :class:`~flash.data.process.Preprocess` provides multiple hooks such as :meth:`~flash.data.process.Preprocess.load_data`
@@ -275,6 +277,17 @@ Example::
 API reference
 *************
 
+.. _data_source:
+
+DataSource
+__________
+
+.. autoclass:: flash.data.data_source.DataSource
+    :members:
+
+
+----------
+
 .. _preprocess:
 
 Preprocess
@@ -325,7 +338,6 @@ __________
 
 .. autoclass:: flash.data.data_module.DataModule
     :members:
-        from_load_data_inputs,
         train_dataset,
         val_dataset,
         test_dataset,

docs/source/reference/image_classification.rst

@@ -183,8 +183,4 @@ ImageClassificationData
 
 .. autoclass:: flash.vision.ImageClassificationData
 
-.. automethod:: flash.vision.ImageClassificationData.from_filepaths
-
-.. automethod:: flash.vision.ImageClassificationData.from_folders
-
 .. autoclass:: flash.vision.ImageClassificationPreprocess

docs/source/reference/tabular_classification.rst

@@ -165,4 +165,4 @@ TabularData
 
 .. automethod:: flash.tabular.TabularData.from_csv
 
-.. automethod:: flash.tabular.TabularData.from_df
+.. automethod:: flash.tabular.TabularData.from_data_frame

docs/source/reference/video_classification.rst

@@ -152,5 +152,3 @@ VideoClassificationData
 -----------------------
 
 .. autoclass:: flash.video.VideoClassificationData
-
-.. automethod:: flash.video.VideoClassificationData.from_paths

flash/core/classification.py

@@ -11,7 +11,6 @@
 # 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.
-from dataclasses import dataclass
 from typing import Any, Callable, List, Mapping, Optional, Sequence, Union
 
 import torch
@@ -20,20 +19,15 @@
 from pytorch_lightning.utilities import rank_zero_warn
 
 from flash.core.model import Task
-from flash.data.process import ProcessState, Serializer
+from flash.data.data_source import LabelsState
+from flash.data.process import Serializer
 
 
 def binary_cross_entropy_with_logits(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
     """Calls BCE with logits and cast the target one_hot (y) encoding to floating point precision."""
     return F.binary_cross_entropy_with_logits(x, y.float())
 
 
-@dataclass(unsafe_hash=True, frozen=True)
-class ClassificationState(ProcessState):
-
-    labels: Optional[List[str]]
-
-
 class ClassificationTask(Task):
 
     def __init__(
@@ -130,7 +124,7 @@ class Labels(Classes):
 
     Args:
         labels: A list of labels, assumed to map the class index to the label for that class. If ``labels`` is not
-            provided, will attempt to get them from the :class:`.ClassificationState`.
+            provided, will attempt to get them from the :class:`.LabelsState`.
 
         multi_label: If true, treats outputs as multi label logits.
 
@@ -141,13 +135,16 @@ def __init__(self, labels: Optional[List[str]] = None, multi_label: bool = False
         super().__init__(multi_label=multi_label, threshold=threshold)
         self._labels = labels
 
+        if labels is not None:
+            self.set_state(LabelsState(labels))
+
     def serialize(self, sample: Any) -> Union[int, List[int], str, List[str]]:
         labels = None
 
         if self._labels is not None:
             labels = self._labels
         else:
-            state = self.get_state(ClassificationState)
+            state = self.get_state(LabelsState)
             if state is not None:
                 labels = state.labels
 
@@ -158,7 +155,5 @@ def serialize(self, sample: Any) -> Union[int, List[int], str, List[str]]:
                 return [labels[cls] for cls in classes]
             return labels[classes]
         else:
-            rank_zero_warn(
-                "No ClassificationState was found, this serializer will act as a Classes serializer.", UserWarning
-            )
+            rank_zero_warn("No LabelsState was found, this serializer will act as a Classes serializer.", UserWarning)
             return classes

flash/core/model.py

@@ -12,15 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import functools
+import inspect
 from importlib import import_module
 from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Tuple, Type, Union
-import inspect
+
 import torch
 import torchmetrics
 from pytorch_lightning import LightningModule
 from pytorch_lightning.callbacks import Callback
 from pytorch_lightning.trainer.states import RunningStage
-from pytorch_lightning.utilities import rank_zero_warn
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from torch import nn
 from torch.optim.lr_scheduler import _LRScheduler
@@ -29,7 +29,8 @@
 from flash.core.registry import FlashRegistry
 from flash.core.schedulers import _SCHEDULERS_REGISTRY
 from flash.core.utils import get_callable_dict
-from flash.data.data_pipeline import DataPipeline
+from flash.data.data_pipeline import DataPipeline, DataPipelineState
+from flash.data.data_source import DataSource, DefaultDataSources
 from flash.data.process import Postprocess, Preprocess, Serializer, SerializerMapping
 
 
@@ -103,6 +104,9 @@ def __init__(
         self._postprocess: Optional[Postprocess] = postprocess
         self._serializer: Optional[Serializer] = None
 
+        # TODO: create enum values to define what are the exact states
+        self._data_pipeline_state: Optional[DataPipelineState] = None
+
         # Explicitly set the serializer to call the setter
         self.serializer = serializer
 
@@ -154,6 +158,7 @@ def test_step(self, batch: Any, batch_idx: int) -> None:
     def predict(
         self,
         x: Any,
+        data_source: str = "default",
         data_pipeline: Optional[DataPipeline] = None,
     ) -> Any:
         """
@@ -169,9 +174,9 @@ def predict(
         """
         running_stage = RunningStage.PREDICTING
 
-        data_pipeline = self.build_data_pipeline(data_pipeline)
+        data_pipeline = self.build_data_pipeline(data_source, data_pipeline)
 
-        x = [x for x in data_pipeline._generate_auto_dataset(x, running_stage)]
+        x = [x for x in data_pipeline._data_source.generate_dataset(x, running_stage)]
         x = data_pipeline.worker_preprocessor(running_stage)(x)
         # switch to self.device when #7188 merge in Lightning
         x = self.transfer_batch_to_device(x, next(self.parameters()).device)
@@ -252,7 +257,11 @@ def serializer(self, serializer: Union[Serializer, Mapping[str, Serializer]]):
             serializer = SerializerMapping(serializer)
         self._serializer = serializer
 
-    def build_data_pipeline(self, data_pipeline: Optional[DataPipeline] = None) -> Optional[DataPipeline]:
+    def build_data_pipeline(
+        self,
+        data_source: Optional[str] = None,
+        data_pipeline: Optional[DataPipeline] = None,
+    ) -> Optional[DataPipeline]:
         """Build a :class:`.DataPipeline` incorporating available
         :class:`~flash.data.process.Preprocess` and :class:`~flash.data.process.Postprocess`
         objects. These will be overridden in the following resolution order (lowest priority first):
@@ -269,20 +278,26 @@ def build_data_pipeline(self, data_pipeline: Optional[DataPipeline] = None) -> O
         Returns:
             The fully resolved :class:`.DataPipeline`.
         """
-        preprocess, postprocess, serializer = None, None, None
+        old_data_source, preprocess, postprocess, serializer = None, None, None, None
 
         # Datamodule
         if self.datamodule is not None and getattr(self.datamodule, 'data_pipeline', None) is not None:
+            old_data_source = getattr(self.datamodule.data_pipeline, '_data_source', None)
             preprocess = getattr(self.datamodule.data_pipeline, '_preprocess_pipeline', None)
             postprocess = getattr(self.datamodule.data_pipeline, '_postprocess_pipeline', None)
             serializer = getattr(self.datamodule.data_pipeline, '_serializer', None)
 
         elif self.trainer is not None and hasattr(
             self.trainer, 'datamodule'
         ) and getattr(self.trainer.datamodule, 'data_pipeline', None) is not None:
+            old_data_source = getattr(self.trainer.datamodule.data_pipeline, '_data_source', None)
             preprocess = getattr(self.trainer.datamodule.data_pipeline, '_preprocess_pipeline', None)
             postprocess = getattr(self.trainer.datamodule.data_pipeline, '_postprocess_pipeline', None)
             serializer = getattr(self.trainer.datamodule.data_pipeline, '_serializer', None)
+        else:
+            # TODO: we should log with low severity level that we use defaults to create
+            # `preprocess`, `postprocess` and `serializer`.
+            pass
 
         # Defaults / task attributes
         preprocess, postprocess, serializer = Task._resolve(
@@ -305,8 +320,16 @@ def build_data_pipeline(self, data_pipeline: Optional[DataPipeline] = None) -> O
                 getattr(data_pipeline, '_serializer', None),
             )
 
-        data_pipeline = DataPipeline(preprocess, postprocess, serializer)
-        data_pipeline.initialize()
+        data_source = data_source or old_data_source
+
+        if isinstance(data_source, str):
+            if preprocess is None:
+                data_source = DataSource()  # TODO: warn the user that we are not using the specified data source
+            else:
+                data_source = preprocess.data_source_of_name(data_source)
+
+        data_pipeline = DataPipeline(data_source, preprocess, postprocess, serializer)
+        self._data_pipeline_state = data_pipeline.initialize(self._data_pipeline_state)
         return data_pipeline
 
     @property
@@ -376,12 +399,16 @@ def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
         # https://pytorch.org/docs/stable/notes/serialization.html
         if self.data_pipeline is not None and 'data_pipeline' not in checkpoint:
             checkpoint['data_pipeline'] = self.data_pipeline
+        if self._data_pipeline_state is not None and '_data_pipeline_state' not in checkpoint:
+            checkpoint['_data_pipeline_state'] = self._data_pipeline_state
         super().on_save_checkpoint(checkpoint)
 
     def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
         super().on_load_checkpoint(checkpoint)
         if 'data_pipeline' in checkpoint:
             self.data_pipeline = checkpoint['data_pipeline']
+        if '_data_pipeline_state' in checkpoint:
+            self._data_pipeline_state = checkpoint['_data_pipeline_state']
 
     @classmethod
     def available_backbones(cls) -> List[str]: