Lightning-Universe · ethanwharris · Nov 8, 2021 · Nov 7, 2021 · Nov 7, 2021 · Nov 7, 2021
@@ -20,6 +20,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
+### Removed
+
+- Removed `OutputMapping` ([#939](https://github.com/PyTorchLightning/lightning-flash/pull/939))
+
+- Removed `Output.enable` and `Output.disable` ([#939](https://github.com/PyTorchLightning/lightning-flash/pull/939))
+
 
 ## [0.5.2] - 2021-11-05
 

@@ -16,7 +16,6 @@ _________________________
     :template: classtemplate.rst
 
     ~flash.core.data.io.output.Output
-    ~flash.core.data.io.output.OutputMapping
 
 flash.core.data.auto_dataset
 ____________________________

@@ -41,7 +41,7 @@ class SpeechRecognition(Task):
         learning_rate: Learning rate to use for training, defaults to ``1e-5``.
         optimizer: Optimizer to use for training.
         lr_scheduler: The LR scheduler to use during training.
-        output: The :class:`~flash.core.data.io.output.Output` to use when serializing prediction outputs.
+        output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
     """
 
     backbones: FlashRegistry = SPEECH_RECOGNITION_BACKBONES

@@ -11,11 +11,10 @@
 # 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 typing import Any, Mapping
+from typing import Any
 
 import torch
 
-import flash
 from flash.core.data.properties import Properties
 from flash.core.data.utils import convert_to_modules
 
@@ -24,18 +23,6 @@ class Output(Properties):
     """An :class:`.Output` encapsulates a single :meth:`~flash.core.data.io.output.Output.transform` method which
     is used to convert the model output into the desired output format when predicting."""
 
-    def __init__(self):
-        super().__init__()
-        self._is_enabled = True
-
-    def enable(self):
-        """Enable output transformation."""
-        self._is_enabled = True
-
-    def disable(self):
-        """Disable output transformation."""
-        self._is_enabled = False
-
     @staticmethod
     def transform(sample: Any) -> Any:
         """Convert the given sample into the desired output format.
@@ -49,28 +36,7 @@ def transform(sample: Any) -> Any:
         return sample
 
     def __call__(self, sample: Any) -> Any:
-        if self._is_enabled:
-            return self.transform(sample)
-        return sample
-
-
-class OutputMapping(Output):
-    """If the model output is a dictionary, then the :class:`.OutputMapping` enables each entry in the dictionary
-    to be passed to it's own :class:`.Output`."""
-
-    def __init__(self, outputs: Mapping[str, Output]):
-        super().__init__()
-
-        self._outputs = outputs
-
-    def transform(self, sample: Any) -> Any:
-        if isinstance(sample, Mapping):
-            return {key: output.transform(sample[key]) for key, output in self._outputs.items()}
-        raise ValueError("The model output must be a mapping when using an OutputMapping.")
-
-    def attach_data_pipeline_state(self, data_pipeline_state: "flash.core.data.data_pipeline.DataPipelineState"):
-        for output in self._outputs.values():
-            output.attach_data_pipeline_state(data_pipeline_state)
+        return self.transform(sample)
 
 
 class _OutputProcessor(torch.nn.Module):

@@ -39,7 +39,7 @@
 from flash.core.data.auto_dataset import BaseAutoDataset
 from flash.core.data.data_pipeline import DataPipeline, DataPipelineState
 from flash.core.data.data_source import DataSource
-from flash.core.data.io.output import Output, OutputMapping
+from flash.core.data.io.output import Output
 from flash.core.data.process import Deserializer, DeserializerMapping, Postprocess, Preprocess
 from flash.core.data.properties import ProcessState
 from flash.core.optimizers.optimizers import _OPTIMIZERS_REGISTRY
@@ -319,8 +319,7 @@ class Task(DatasetProcessor, ModuleWrapperBase, LightningModule, metaclass=Check
             deserialize the input
         preprocess: :class:`~flash.core.data.process.Preprocess` to use as the default for this task.
         postprocess: :class:`~flash.core.data.process.Postprocess` to use as the default for this task.
-        output: Either a single :class:`~flash.core.data.io.output.Output` or a mapping of these to
-            serialize the output e.g. convert the model output into the desired output format when predicting.
+        output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
     """
 
     optimizers: FlashRegistry = _OPTIMIZERS_REGISTRY
@@ -630,9 +629,7 @@ def output(self) -> Optional[Output]:
 
     @torch.jit.unused
     @output.setter
-    def output(self, output: Union[Output, Mapping[str, Output]]):
-        if isinstance(output, Mapping):
-            output = OutputMapping(output)
+    def output(self, output: Output):
         self._output = output
 
     @torch.jit.unused
@@ -662,7 +659,7 @@ def serializer(self) -> Optional[Output]:
         "It will be removed in v%(remove_in)s.",
         stream=functools.partial(warn, category=FutureWarning),
     )
-    def serializer(self, serializer: Union[Output, Mapping[str, Output]]):
+    def serializer(self, serializer: Output):
         self.output = serializer
 
     def build_data_pipeline(

@@ -16,4 +16,4 @@
 DESERIALIZER_TYPE = Optional[Union[Deserializer, Mapping[str, Deserializer]]]
 PREPROCESS_TYPE = Optional[Preprocess]
 POSTPROCESS_TYPE = Optional[Postprocess]
-OUTPUT_TYPE = Optional[Union[Output, Mapping[str, Output]]]
+OUTPUT_TYPE = Optional[Output]
@@ -60,8 +60,7 @@ def fn_resnet(pretrained: bool = True):
             `metric(preds,target)` and return a single scalar tensor. Defaults to :class:`torchmetrics.Accuracy`.
         learning_rate: Learning rate to use for training, defaults to ``1e-3``.
         multi_label: Whether the targets are multi-label or not.
-        output: A instance of :class:`~flash.core.data.io.output.Output` or a mapping consisting of such
-            to use when serializing prediction outputs.
+        output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
         training_strategy: string indicating the training strategy. Adjust if you want to use `learn2learn`
             for doing meta-learning research
         training_strategy_kwargs: Additional kwargs for setting the training strategy

@@ -42,8 +42,7 @@ class ObjectDetector(AdapterTask):
         pretrained: Whether the model from torchvision should be loaded with it's pretrained weights.
             Has no effect for custom models.
         learning_rate: The learning rate to use for training
-        output: A instance of :class:`~flash.core.data.io.output.Output` or a mapping consisting of such
-            to use when serializing prediction outputs.
+        output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
         kwargs: additional kwargs nessesary for initializing the backbone task
     """
 

@@ -68,7 +68,7 @@ class SemanticSegmentation(ClassificationTask):
             `metric(preds,target)` and return a single scalar tensor. Defaults to :class:`torchmetrics.IOU`.
         learning_rate: Learning rate to use for training.
         multi_label: Whether the targets are multi-label or not.
-        output: The :class:`~flash.core.data.io.output.Output` to use when serializing prediction outputs.
+        output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
         postprocess: :class:`~flash.core.data.process.Postprocess` use for post processing samples.
     """
 

@@ -53,7 +53,7 @@ class PointCloudObjectDetector(Task):
             by the :class:`~flash.core.classification.ClassificationTask` depending on the ``multi_label`` argument.
         learning_rate: The learning rate for the optimizer.
         multi_label: If ``True``, this will be treated as a multi-label classification problem.
-        output: The :class:`~flash.core.data.io.output.Output` to use for prediction outputs.
+        output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
         lambda_loss_cls: The value to scale the loss classification.
         lambda_loss_bbox: The value to scale the bounding boxes loss.
         lambda_loss_dir: The value to scale the bounding boxes direction loss.

@@ -84,7 +84,7 @@ class PointCloudSegmentation(ClassificationTask):
             by the :class:`~flash.core.classification.ClassificationTask` depending on the ``multi_label`` argument.
         learning_rate: The learning rate for the optimizer.
         multi_label: If ``True``, this will be treated as a multi-label classification problem.
-        output: The :class:`~flash.core.data.io.output.Output` to use for prediction outputs.
+        output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
     """
 
     backbones: FlashRegistry = POINTCLOUD_SEGMENTATION_BACKBONES

@@ -42,7 +42,7 @@ class TabularClassifier(ClassificationTask):
             `metric(preds,target)` and return a single scalar tensor. Defaults to :class:`torchmetrics.Accuracy`.
         learning_rate: Learning rate to use for training.
         multi_label: Whether the targets are multi-label or not.
-        output: The :class:`~flash.core.data.io.output.Output` to use when serializing prediction outputs.
+        output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
         **tabnet_kwargs: Optional additional arguments for the TabNet model, see
             `pytorch_tabnet <https://dreamquark-ai.github.io/tabnet/_modules/pytorch_tabnet/tab_network.html#TabNet>`_.
     """

@@ -40,7 +40,7 @@ class TabularRegressor(RegressionTask):
             `metric(preds,target)` and return a single scalar tensor.
         learning_rate: Learning rate to use for training.
         multi_label: Whether the targets are multi-label or not.
-        serializer: The :class:`~flash.core.data.process.Serializer` to use when serializing prediction outputs.
+        output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
         **tabnet_kwargs: Optional additional arguments for the TabNet model, see
             `pytorch_tabnet <https://dreamquark-ai.github.io/tabnet/_modules/pytorch_tabnet/tab_network.html#TabNet>`_.
     """

@@ -40,7 +40,7 @@ class TemplateSKLearnClassifier(ClassificationTask):
             by the :class:`~flash.core.classification.ClassificationTask` depending on the ``multi_label`` argument.
         learning_rate: The learning rate for the optimizer.
         multi_label: If ``True``, this will be treated as a multi-label classification problem.
-        output: The :class:`~flash.core.data.io.output.Output` to use for prediction outputs.
+        output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
     """
 
     backbones: FlashRegistry = TEMPLATE_BACKBONES

@@ -46,7 +46,7 @@ class TextClassifier(ClassificationTask):
             `metric(preds,target)` and return a single scalar tensor. Defaults to :class:`torchmetrics.Accuracy`.
         learning_rate: Learning rate to use for training, defaults to `1e-3`
         multi_label: Whether the targets are multi-label or not.
-        output: The :class:`~flash.core.data.io.output.Output` to use when serializing prediction outputs.
+        output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
         enable_ort: Enable Torch ONNX Runtime Optimization: https://onnxruntime.ai/docs/#onnx-runtime-for-training
     """
 

@@ -93,8 +93,7 @@ class VideoClassifier(ClassificationTask):
         head: either a `nn.Module` or a callable function that converts the features extrated from the backbone
             into class log probabilities (assuming default loss function). If `None`, will default to using
             a single linear layer.
-        output: A instance of :class:`~flash.core.data.io.output.Output` that determines how the output
-            should be serialized e.g. convert the model output into the desired output format when predicting.
+        output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
     """
 
     backbones: FlashRegistry = _VIDEO_CLASSIFIER_BACKBONES

@@ -14,33 +14,25 @@
 import os
 from unittest.mock import Mock
 
-import pytest
 import torch
 from torch.utils.data import DataLoader
 
 from flash.core.classification import Labels
 from flash.core.data.data_pipeline import DataPipeline, DataPipelineState
 from flash.core.data.data_source import LabelsState
-from flash.core.data.io.output import Output, OutputMapping
+from flash.core.data.io.output import Output
 from flash.core.data.process import DefaultPreprocess
-from flash.core.data.properties import ProcessState
 from flash.core.model import Task
 from flash.core.trainer import Trainer
 
 
-def test_output_enable_disable():
-    """Tests that ``Output`` can be enabled and disabled correctly."""
-
+def test_output():
+    """Tests basic ``Output`` methods."""
     my_output = Output()
 
     assert my_output.transform("test") == "test"
-    my_output.transform = Mock()
-
-    my_output.disable()
-    assert my_output("test") == "test"
-    my_output.transform.assert_not_called()
 
-    my_output.enable()
+    my_output.transform = Mock()
     my_output("test")
     my_output.transform.assert_called_once()
 
@@ -65,45 +57,3 @@ def __init__(self):
     model = CustomModel.load_from_checkpoint(checkpoint_file)
     assert isinstance(model._data_pipeline_state, DataPipelineState)
     assert model._data_pipeline_state._state[LabelsState] == LabelsState(["a", "b"])
-
-
-def test_output_mapping():
-    """Tests that ``OutputMapping`` correctly passes its inputs to the underlying outputs.
-
-    Also checks that state is retrieved / loaded correctly.
-    """
-
-    output1 = Output()
-    output1.transform = Mock(return_value="test1")
-
-    class output1State(ProcessState):
-        pass
-
-    output2 = Output()
-    output2.transform = Mock(return_value="test2")
-
-    class output2State(ProcessState):
-        pass
-
-    output_mapping = OutputMapping({"key1": output1, "key2": output2})
-    assert output_mapping({"key1": "output1", "key2": "output2"}) == {"key1": "test1", "key2": "test2"}
-    output1.transform.assert_called_once_with("output1")
-    output2.transform.assert_called_once_with("output2")
-
-    with pytest.raises(ValueError, match="output must be a mapping"):
-        output_mapping("not a mapping")
-
-    output1_state = output1State()
-    output2_state = output2State()
-
-    output1.set_state(output1_state)
-    output2.set_state(output2_state)
-
-    data_pipeline_state = DataPipelineState()
-    output_mapping.attach_data_pipeline_state(data_pipeline_state)
-
-    assert output1._data_pipeline_state is data_pipeline_state
-    assert output2._data_pipeline_state is data_pipeline_state
-
-    assert data_pipeline_state.get_state(output1State) is output1_state
-    assert data_pipeline_state.get_state(output2State) is output2_state