Adds a template task and docs

CHANGELOG.md

@@ -23,6 +23,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added Semantic Segmentation task ([#239](https://github.com/PyTorchLightning/lightning-flash/pull/239) [#287](https://github.com/PyTorchLightning/lightning-flash/pull/287) [#290](https://github.com/PyTorchLightning/lightning-flash/pull/290))
 - Added Object detection prediction example ([#283](https://github.com/PyTorchLightning/lightning-flash/pull/283))
 - Added Style Transfer task and accompanying finetuning and prediction examples ([#262](https://github.com/PyTorchLightning/lightning-flash/pull/262))
+- Added a Template task and tutorials showing how to contribute a task to flash ([#306](https://github.com/PyTorchLightning/lightning-flash/pull/306))
 
 ### Changed
 

docs/source/conf.py

@@ -90,6 +90,7 @@ def _load_py_module(fname, pkg="flash"):
     "torch": ("https://pytorch.org/docs/stable/", None),
     "numpy": ("https://docs.scipy.org/doc/numpy/", None),
     "PIL": ("https://pillow.readthedocs.io/en/stable/", None),
+    "pytorchvideo": ("https://pytorchvideo.readthedocs.io/en/latest/", None),
     "pytorch_lightning": ("https://pytorch-lightning.readthedocs.io/en/stable/", None),
 }
 

docs/source/custom_task.rst

@@ -4,6 +4,7 @@ Tutorial: Creating a Custom Task
 In this tutorial we will go over the process of creating a custom :class:`~flash.core.model.Task`,
 along with a custom :class:`~flash.core.data.data_module.DataModule`.
 
+.. note:: This tutorial is only intended to help you create a small custom task for a personal project. If you want a more detailed guide, have a look at our :ref:`guide on contributing a task to flash. <contributing>`
 
 The tutorial objective is to create a ``RegressionTask`` to learn to predict if someone has ``diabetes`` or not.
 We will use ``scikit-learn`` `Diabetes dataset <https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset>`__.

docs/source/index.rst

@@ -50,6 +50,25 @@ Lightning Flash
    general/finetuning
    general/predictions
 
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Contributing a Task
+
+   template/intro
+   template/data
+   template/backbones
+   template/task
+   template/optional
+   template/examples
+   template/tests
+   template/docs
+
+.. toctree::
+   :hidden:
+
+   reference/template
+
 Indices and tables
 ==================
 

docs/source/reference/template.rst

@@ -0,0 +1,75 @@
+
+.. _template:
+
+########
+Template
+########
+
+********
+The task
+********
+
+Here you should add a description of your task. For example:
+Classification is the task of assigning one of a number of classes to each data point.
+The :class:`~flash.template.TemplateSKLearnClassifier` is a :class:`~flash.core.model.Task` for classifying the datasets included with scikit-learn.
+
+------
+
+*********
+Inference
+*********
+
+Here, you should add a short intro to your predict example, and then use ``literalinclude`` to add it.
+
+.. note:: We skip the first 14 lines as they are just the copyright notice.
+
+Our predict example uses a model pre-trained on the Iris data.
+
+.. literalinclude:: ../../../flash_examples/predict/template.py
+    :language: python
+    :lines: 14-
+
+For more advanced inference options, see :ref:`predictions`.
+
+------
+
+********
+Training
+********
+
+In this section, we briefly describe the data, and then ``literalinclude`` our finetuning example.
+
+Now we'll train on Fisher's classic Iris data.
+It contains 150 records with four features (sepal length, sepal width, petal length, and petal width) in three classes (species of Iris: setosa, virginica and versicolor).
+
+Now all we need is to train our task!
+
+.. literalinclude:: ../../../flash_examples/finetuning/template.py
+    :language: python
+    :lines: 14-
+
+------
+
+*************
+API reference
+*************
+
+We usually include the API reference for the :class:`~flash.core.model.Task` and :class:`~flash.core.data.data_module.DataModule`.
+You can optionally add the other classes you've implemented.
+To add the API reference, use the ``autoclass`` directive.
+
+.. _template_classifier:
+
+TemplateSKLearnClassifier
+-------------------------
+
+.. autoclass:: flash.template.TemplateSKLearnClassifier
+    :members:
+    :exclude-members: forward
+
+.. _template_data:
+
+TemplateData
+------------
+
+.. autoclass:: flash.template.TemplateData

docs/source/template/backbones.rst

@@ -0,0 +1,43 @@
+.. _contributing_backbones:
+
+*************
+The Backbones
+*************
+
+Now that you've got a way of loading data, you should implement some backbones to use with your :class:`~flash.core.model.Task`.
+Create a :any:`FlashRegistry <registry>` to use with your :class:`~flash.core.model.Task` in `backbones.py <https://github.com/PyTorchLightning/lightning-flash/blob/master/flash/template/classification/backbones.py>`_.
+
+The registry allows you to register backbones for your task that can be selected by the user.
+The backbones can come from anywhere as long as you can register a function that loads the backbone.
+Furthermore, the user can add their own models to the existing backbones, without having to write their own :class:`~flash.core.model.Task`!
+
+You can create a registry like this:
+
+.. code-block:: python
+
+    TEMPLATE_BACKBONES = FlashRegistry("backbones")
+
+Let's add a simple MLP backbone to our registry.
+We need a function that creates the backbone and returns it along with the output size (so that we can create the model head in our :class:`~flash.core.model.Task`).
+You can use any name for the function, although we use ``load_{model name}`` by convention.
+Here's the code:
+
+.. literalinclude:: ../../../flash/template/classification/backbones.py
+    :language: python
+    :pyobject: load_mlp_128
+
+Here's another example with a slightly more complex model:
+
+.. literalinclude:: ../../../flash/template/classification/backbones.py
+    :language: python
+    :pyobject: load_mlp_128_256
+
+Here's a more advanced example, which adds ``SimCLR`` to the ``IMAGE_CLASSIFIER_BACKBONES``, from `flash/image/backbones.py <https://github.com/PyTorchLightning/lightning-flash/blob/master/flash/image/backbones.py>`_:
+
+.. literalinclude:: ../../../flash/image/backbones.py
+    :language: python
+    :pyobject: load_simclr_imagenet
+
+------
+
+Once you've got some data and some backbones, :ref:`implement your task! <contributing_task>`

docs/source/template/data.rst

@@ -0,0 +1,212 @@
+.. _contributing_data:
+
+********
+The Data
+********
+
+The first step to contributing a task is to implement the classes we need to load some data.
+Inside `data.py <https://github.com/PyTorchLightning/lightning-flash/blob/master/flash/template/classification/data.py>`_ you should implement:
+
+#. some :class:`~flash.core.data.data_source.DataSource` classes *(optional)*
+#. a :class:`~flash.core.data.process.Preprocess`
+#. a :class:`~flash.core.data.data_module.DataModule`
+#. a :class:`~flash.core.data.base_viz.BaseVisualization` *(optional)*
+#. a :class:`~flash.core.data.process.Postprocess` *(optional)*
+
+DataSource
+^^^^^^^^^^
+
+The :class:`~flash.core.data.data_source.DataSource` class contains the logic for data loading from different sources such as folders, files, tensors, etc.
+Every Flash :class:`~flash.core.data.data_module.DataModule` can be instantiated with :meth:`~flash.core.data.data_module.DataModule.from_datasets`.
+For each additional way you want the user to be able to instantiate your :class:`~flash.core.data.data_module.DataModule`, you'll need to create a :class:`~flash.core.data.data_source.DataSource`.
+Each :class:`~flash.core.data.data_source.DataSource` has 2 methods:
+
+- :meth:`~flash.core.data.data_source.DataSource.load_data` takes some dataset metadata (e.g. a folder name) as input and produces a sequence or iterable of samples or sample metadata.
+- :meth:`~flash.core.data.data_source.DataSource.load_sample` then takes as input a single element from the output of ``load_data`` and returns a sample.
+
+By default these methods just return their input, so you don't need both a :meth:`~flash.core.data.data_source.DataSource.load_data` and a :meth:`~flash.core.data.data_source.DataSource.load_sample` to create a :class:`~flash.core.data.data_source.DataSource`.
+Where possible, you should override one of our existing :class:`~flash.core.data.data_source.DataSource` classes.
+
+Let's start by implementing a ``TemplateNumpyDataSource``, which overrides :class:`~flash.core.data.data_source.NumpyDataSource`.
+The main :class:`~flash.core.data.data_source.DataSource` method that we have to implement is :meth:`~flash.core.data.data_source.DataSource.load_data`.
+As we're extending the ``NumpyDataSource``, we expect the same ``data`` argument (in this case, a tuple containing data and corresponding target arrays).
+
+We can also take the dataset argument.
+Any attributes we set on ``dataset`` will be available on the :class:`~torch.utils.data.Dataset` generated by our :class:`~flash.core.data.data_source.DataSource`.
+In this data source, we'll set the ``num_features`` attribute.
+
+Here's the code for our ``TemplateNumpyDataSource.load_data`` method:
+
+.. literalinclude:: ../../../flash/template/classification/data.py
+    :language: python
+    :dedent: 4
+    :pyobject: TemplateNumpyDataSource.load_data
+
+.. note:: Later, when we add :ref:`our DataModule implementation <contributing_data_module>`, we'll make ``num_features`` available to the user.
+
+Sometimes you need to something a bit more custom.
+When creating a custom :class:`~flash.core.data.data_source.DataSource`, the type of the ``data`` argument is up to you.
+For our template :class:`~flash.core.data.model.Task`, it would be cool if the user could provide a scikit-learn ``Bunch`` as the data source.
+To achieve this, we'll add a ``TemplateSKLearnDataSource`` whose ``load_data`` expects a ``Bunch`` as input.
+We override our ``TemplateNumpyDataSource`` so that we can call ``super`` with the data and targets extracted from the ``Bunch``.
+We perform two additional steps here to improve the user experience:
+
+1. We set the ``num_classes`` attribute on the ``dataset``. If ``num_classes`` is set, it is automatically made available as a property of the :class:`~flash.core.data.data_module.DataModule`.
+2. We create and set a :class:`~flash.core.data.data_source.LabelsState`. The labels provided here will be shared with the :class:`~flash.core.classification.Labels` serializer, so the user doesn't need to provide them.
+
+Here's the code for the ``TemplateSKLearnDataSource.load_data`` method:
+
+.. literalinclude:: ../../../flash/template/classification/data.py
+    :language: python
+    :dedent: 4
+    :pyobject: TemplateSKLearnDataSource.load_data
+
+We can customize the behaviour of our :meth:`~flash.core.data.data_source.DataSource.load_data` for different stages, by prepending `train`, `val`, `test`, or `predict`.
+For our ``TemplateSKLearnDataSource``, we don't want to provide any targets to the model when predicting.
+We can implement ``predict_load_data`` like this:
+
+.. literalinclude:: ../../../flash/template/classification/data.py
+    :language: python
+    :dedent: 4
+    :pyobject: TemplateSKLearnDataSource.predict_load_data
+
+DataSource vs Dataset
+~~~~~~~~~~~~~~~~~~~~~
+
+A :class:`~flash.core.data.data_source.DataSource` is not the same as a :class:`torch.utils.data.Dataset`.
+When a ``from_*`` method is called on your :class:`~flash.core.data.data_module.DataModule`, it gets the :class:`~flash.core.data.data_source.DataSource` to use from the :class:`~flash.core.data.process.Preprocess`.
+A :class:`~torch.utils.data.Dataset` is then created from the :class:`~flash.core.data.data_source.DataSource` for each stage (`train`, `val`, `test`, `predict`) using the provided metadata (e.g. folder name, numpy array etc.).
+
+The output of the :meth:`~flash.core.data.data_source.DataSource.load_data` can just be a :class:`torch.utils.data.Dataset` instance.
+If the library that your :class:`~flash.core.data.model.Task` is based on provides a custom dataset, you don't need to re-write it as a :class:`~flash.core.data.data_source.DataSource`.
+For example, the :meth:`~flash.core.data.data_source.DataSource.load_data` of the ``VideoClassificationPathsDataSource`` just creates an :class:`~pytorchvideo.data.EncodedVideoDataset` from the given folder.
+Here's how it looks (from `video/classification.data.py <https://github.com/PyTorchLightning/lightning-flash/blob/master/flash/video/classification/data.py>`_):
+
+.. literalinclude:: ../../../flash/video/classification/data.py
+    :language: python
+    :dedent: 4
+    :pyobject: VideoClassificationPathsDataSource.load_data
+
+Preprocess
+^^^^^^^^^^
+
+The :class:`~flash.core.data.process.Preprocess` object contains all the data transforms.
+Internally we inject the :class:`~flash.core.data.process.Preprocess` transforms at several points along the pipeline.
+
+Defining the standard transforms (typically at least a ``to_tensor_transform`` should be defined) for your :class:`~flash.core.data.process.Preprocess` is as simple as implementing the ``default_transforms`` method.
+The :class:`~flash.core.data.process.Preprocess` must take ``train_transform``, ``val_transform``, ``test_transform``, and ``predict_transform`` arguments in the ``__init__``.
+These arguments can be provided by the user (when creating the :class:`~flash.core.data.data_module.DataModule`) to override the default transforms.
+Any additional arguments are up to you.
+
+Inside the ``__init__``, we make a call to super.
+This is where we register our data sources.
+Data sources should be given as a dictionary which maps data source name to data source object.
+The name can be anything, but if you want to take advantage of our built-in ``from_*`` classmethods, you should use :class:`~flash.core.data.data_source.DefaultDataSources` as the names.
+In our case, we have both a :attr:`~flash.core.data.data_source.DefaultDataSources.NUMPY` and a custom scikit-learn data source (which we'll call `"sklearn"`).
+
+You should also provide a ``default_data_source``.
+This is the name of the data source to use by default when predicting.
+It'd be cool if we could get predictions just from a numpy array, so we'll use :attr:`~flash.core.data.data_source.DefaultDataSources.NUMPY` as the default.
+
+Here's our ``TemplatePreprocess.__init__``:
+
+.. literalinclude:: ../../../flash/template/classification/data.py
+    :language: python
+    :dedent: 4
+    :pyobject: TemplatePreprocess.__init__
+
+For our ``TemplatePreprocess``, we'll just configure a default ``to_tensor_transform``.
+Let's first define the transform as a ``staticmethod``:
+
+.. literalinclude:: ../../../flash/template/classification/data.py
+    :language: python
+    :dedent: 4
+    :pyobject: TemplatePreprocess.input_to_tensor
+
+Our inputs samples will be dictionaries whose keys are in the :class:`~flash.core.data.data_source.DefaultDataKeys`.
+You can map each key to different transforms using :class:`~flash.core.data.transforms.ApplyToKeys`.
+Here's our ``default_transforms`` method:
+
+.. literalinclude:: ../../../flash/template/classification/data.py
+    :language: python
+    :dedent: 4
+    :pyobject: TemplatePreprocess.default_transforms
+
+.. _contributing_data_module:
+
+DataModule
+^^^^^^^^^^
+
+The :class:`~flash.core.data.data_module.DataModule` is responsible for creating the :class:`~torch.utils.data.DataLoader` and injecting the transforms for each stage.
+When the user calls a ``from_*`` method (such as :meth:`~flash.core.data.data_module.DataModule.from_numpy`), the following steps take place:
+
+#. The :meth:`~flash.core.data.data_module.DataModule.from_data_source` method is called with the name of the :class:`~flash.core.data.data_source.DataSource` to use and the inputs to provide to :meth:`~flash.core.data.data_source.DataSource.load_data` for each stage.
+#. The :class:`~flash.core.data.process.Preprocess` is created from ``cls.preprocess_cls`` (if it wasn't provided by the user) with any provided transforms.
+#. The :class:`~flash.core.data.data_source.DataSource` of the provided name is retrieved from the :class:`~flash.core.data.process.Preprocess`.
+#. A :class:`~flash.core.data.auto_dataset.BaseAutoDataset` is created from the :class:`~flash.core.data.data_source.DataSource` for each stage.
+#. The :class:`~flash.core.data.data_module.DataModule` is instantiated with the data sets.
+
+|
+
+To create our ``TemplateData`` :class:`~flash.core.data.data_module.DataModule`, we first need to attach out preprocess class like this:
+
+.. code-block:: python
+
+    preprocess_cls = TemplatePreprocess
+
+Since we provided a :attr:`~flash.core.data.data_source.DefaultDataSources.NUMPY` :class:`~flash.core.data.data_source.DataSource` in the ``TemplatePreprocess``, :meth:`~flash.core.data.data_module.DataModule.from_numpy` will now work with our ``TemplateData``.
+
+If you've defined a fully custom :class:`~flash.core.data.data_source.DataSource` (like our ``TemplateSKLearnDataSource``), then you will need to write a ``from_*`` method for each.
+Here's the ``from_sklearn`` method for our ``TemplateData``:
+
+.. literalinclude:: ../../../flash/template/classification/data.py
+    :language: python
+    :dedent: 4
+    :pyobject: TemplateData.from_sklearn
+
+The final step is to implement the ``num_features`` property for our ``TemplateData``.
+This is just a convenience for the user that finds the ``num_features`` attribute on any of the data sets and returns it.
+Here's the code:
+
+.. literalinclude:: ../../../flash/template/classification/data.py
+    :language: python
+    :dedent: 4
+    :pyobject: TemplateData.num_features
+
+BaseVisualization
+^^^^^^^^^^^^^^^^^
+
+An optional step is to implement a :class:`~flash.core.data.base_viz.BaseVisualization`.
+The :class:`~flash.core.data.base_viz.BaseVisualization` lets you control how data at various points in the pipeline can be visualized.
+This is extremely useful for debugging purposes, allowing users to view their data and understand the impact of their transforms.
+
+.. note::
+    Don't worry about implementing it right away, you can always come back and add it later!
+
+Here's the code for our ``TemplateVisualization`` which just prints the data:
+
+.. literalinclude:: ../../../flash/template/classification/data.py
+    :language: python
+    :pyobject: TemplateVisualization
+
+We can configure our custom visualization in the ``TemplateData`` using :meth:`~flash.core.data.data_module.DataModule.configure_data_fetcher` like this:
+
+.. literalinclude:: ../../../flash/template/classification/data.py
+    :language: python
+    :dedent: 4
+    :pyobject: TemplateData.configure_data_fetcher
+
+Postprocess
+^^^^^^^^^^^
+
+:class:`~flash.core.data.process.Postprocess` contains any transforms that need to be applied *after* the model.
+You may want to use it for: converting tokens back into text, applying an inverse normalization to an output image, resizing a generated image back to the size of the input, etc.
+As an example, here's the :class:`~text.classification.data.TextClassificationPostProcess` which gets the logits from a ``SequenceClassifierOutput``:
+
+.. literalinclude:: ../../../flash/text/classification/data.py
+    :language: python
+    :pyobject: TextClassificationPostProcess
+
+------
+
+Now that you've got some data, it's time to :ref:`add some backbones for your task! <contributing_backbones>`