Refactor preprocess_cls to preprocess, add Serializer, add `Dat…

…aPipelineState` (#229) * Initial commit * Initial commit * Small fixes * Small fixes * Fix a small bug * Update docs * Update notebook * Update finetuning image classification * Updates * Update docs and serializer mapping * Fix missed merge conflicts * Fix some broken tests * Fix a test * Fix a test * Fix some tests * Fix a test * Update examples * Update examples * Pre-commit * Pre-commit * Update text classification * Add a test * Multi-label example initial commit * Add predict example for multi_label * Remove unused imports * Update predict example * Update examples * Add multi-label Labels suport * Update test_classification * Update .gitignore * Add some tests * Fix broken test * Update test_process * Some docs updates * Update docs * Fix some tests * Add back some process_cls * Add types * Update docs/source/general/data.rst Co-authored-by: thomas chaton <thomas@grid.ai> * Update flash/data/process.py Co-authored-by: Edgar Riba <edgar.riba@gmail.com> * Add comment * Update example * Remove state checkpoint not needed * Fix doctest * Update image_classification example * Update following fix * Fix num-workers in test_examples * Update example predict * Better fix for windows error Co-authored-by: thomas chaton <thomas@grid.ai> Co-authored-by: Edgar Riba <edgar.riba@gmail.com>
Lightning-Universe · Apr 22, 2021 · 1ab7346 · 1ab7346
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diff --git a/.gitignore b/.gitignore
@@ -1,3 +1,4 @@
+.DS_Store
 .lock
 lightning_logs
 
@@ -149,3 +150,4 @@ xsum
 coco128
 wmt_en_ro
 kinetics
+movie_posters
diff --git a/docs/source/custom_task.rst b/docs/source/custom_task.rst
@@ -115,13 +115,13 @@ We will define a custom ``NumpyDataModule`` class subclassing :class:`~flash.dat
 This ``NumpyDataModule`` class will provide a ``from_xy_dataset`` helper ``classmethod`` to instantiate
 :class:`~flash.data.data_module.DataModule` from x, y numpy arrays.
 
-Here is how it would look like:
+Here is how it would look:
 
 Example::
 
     x, y = ...
-    preprocess_cls = ...
-    datamodule = NumpyDataModule.from_xy_dataset(x, y, preprocess_cls)
+    preprocess = ...
+    datamodule = NumpyDataModule.from_xy_dataset(x, y, preprocess)
 
 Here is the ``NumpyDataModule`` implementation:
 
@@ -140,12 +140,12 @@ Example::
             cls,
             x: ND,
             y: ND,
-            preprocess_cls: Preprocess = NumpyPreprocess,
+            preprocess: Preprocess = None,
             batch_size: int = 64,
             num_workers: int = 0
         ):
 
-            preprocess = preprocess_cls()
+            preprocess = preprocess or NumpyPreprocess()
 
             x_train, x_test, y_train, y_test = train_test_split(
                 x, y, test_size=.20, random_state=0)
@@ -180,7 +180,7 @@ It allows the user much more granular control over their data processing flow.
 
 .. note::
 
-    Why introducing :class:`~flash.data.process.Preprocess` ?
+    Why introduce :class:`~flash.data.process.Preprocess` ?
 
     The :class:`~flash.data.process.Preprocess` object reduces the engineering overhead to make inference on raw data or
     to deploy the model in production environnement compared to traditional

diff --git a/docs/source/general/data.rst b/docs/source/general/data.rst
@@ -29,7 +29,9 @@ Here are common terms you need to be familiar with:
         The :class:`~flash.data.process.Preprocess` hooks covers from data-loading to model forwarding.
    * - :class:`~flash.data.process.Postprocess`
      - The :class:`~flash.data.process.Postprocess` provides a simple hook-based API to encapsulate your post-processing logic.
-        The :class:`~flash.data.process.Postprocess` hooks covers from model outputs to predictions export.
+        The :class:`~flash.data.process.Postprocess` hooks cover from model outputs to predictions export.
+   * - :class:`~flash.data.process.Serializer`
+     - The :class:`~flash.data.process.Serializer` provides a single ``serialize`` method that is used to convert model outputs (after the :class:`~flash.data.process.Postprocess`) to the desired output format during prediction.
 
 *******************************************
 How to use out-of-the-box flashdatamodules
@@ -49,7 +51,9 @@ However, after model training, it requires a lot of engineering overhead to make
 Usually, extra processing logic should be added to bridge the gap between training data and raw data.
 
 The :class:`~flash.data.process.Preprocess` and :class:`~flash.data.process.Postprocess` classes can be used to
-store the data as well as the preprocessing and postprocessing transforms.
+store the data as well as the preprocessing and postprocessing transforms. The :class:`~flash.data.process.Serializer`
+class provides the logic for converting :class:`~flash.data.process.Postprocess` outputs to the desired predict format
+(e.g. classes, labels, probabilites, etc.).
 
 By providing a series of hooks that can be overridden with custom data processing logic,
 the user has much more granular control over their data processing flow.
@@ -122,7 +126,7 @@ Example::
         train_folder="data/hymenoptera_data/train/",
         val_folder="data/hymenoptera_data/val/",
         test_folder="data/hymenoptera_data/test/",
-        preprocess_cls=CustomImageClassificationPreprocess
+        preprocess=CustomImageClassificationPreprocess(),
     )
 
 
@@ -157,7 +161,7 @@ Example::
         val_folder="./data/val",
         test_folder="./data/test",
         predict_folder="./data/predict",
-        preprocess=preprocess
+        preprocess=preprocess,
     )
 
     model = ImageClassifier(...)
@@ -190,6 +194,7 @@ Example::
             **kwargs
         ):
 
+            # Set a custom ``Preprocess`` if none was provided
             preprocess = preprocess or cls.preprocess_cls()
 
             # {stage}_load_data_input will be given to your
@@ -291,6 +296,18 @@ ___________
     :members:
 
 
+----------
+
+.. _serializer:
+
+Serializer
+___________
+
+
+.. autoclass:: flash.data.process.Serializer
+    :members:
+
+
 ----------
 
 .. _datapipeline:
@@ -414,16 +431,18 @@ Example::
     predictions = lightning_module(data)
 
 
-Postprocess
-___________
+Postprocess and Serializer
+__________________________
 
 
-Once the predictions have been generated by the Flash :class:`~flash.core.model.Task`.
-The Flash :class:`~flash.data.data_pipeline.DataPipeline` will behind the scenes execute the :class:`~flash.data.process.Postprocess` hooks.
+Once the predictions have been generated by the Flash :class:`~flash.core.model.Task`, the Flash
+:class:`~flash.data.data_pipeline.DataPipeline` will execute the :class:`~flash.data.process.Postprocess` hooks and the
+:class:`~flash.data.process.Serializer` behind the scenes.
 
-First, the ``per_batch_transform`` hooks will be applied on the batch predictions.
-Then the ``uncollate`` will split the batch into individual predictions.
-Finally, the ``per_sample_transform`` will be applied on each prediction.
+First, the :meth:`~flash.data.process.Postprocess.per_batch_transform` hooks will be applied on the batch predictions.
+Then, the :meth:`~flash.data.process.Postprocess.uncollate` will split the batch into individual predictions.
+Next, the :meth:`~flash.data.process.Postprocess.per_sample_transform` will be applied on each prediction.
+Finally, the :meth:`~flash.data.process.Serializer.serialize` method will be called to serialize the predictions.
 
 .. note:: The transform can be applied either on device or ``CPU``.
 
@@ -438,7 +457,9 @@ Example::
 
         samples = uncollate(batch)
 
-        return [per_sample_transform(sample) for sample in samples]
+        samples = [per_sample_transform(sample) for sample in samples]
+        # only if serializers are enabled.
+        return [serialize(sample) for sample in samples]
 
     predictions = lightning_module(data)
     return uncollate_fn(predictions)
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -22,6 +22,7 @@ Lightning Flash
    reference/task
    reference/image_classification
    reference/image_embedder
+   reference/multi_label_classification
    reference/summarization
    reference/text_classification
    reference/tabular_classification

diff --git a/docs/source/reference/multi_label_classification.rst b/docs/source/reference/multi_label_classification.rst
@@ -0,0 +1,212 @@
+
+.. _multi_label_classification:
+
+################################
+Multi-label Image Classification
+################################
+
+********
+The task
+********
+Multi-label classification is the task of assigning a number of labels from a fixed set to each data point, which can be in any modality. In this example, we will look at the task of trying to predict the movie genres from an image of the movie poster.
+
+------
+
+********
+The data
+********
+The data we will use in this example is a subset of the awesome movie poster genre prediction data set from the paper "Movie Genre Classification based on Poster Images with Deep Neural Networks" by Wei-Ta Chu and Hung-Jui Guo, resized to 128 by 128.
+Take a look at their paper (and please consider citing their paper if you use the data) here: `www.cs.ccu.edu.tw/~wtchu/projects/MoviePoster/ <https://www.cs.ccu.edu.tw/~wtchu/projects/MoviePoster/>`_.
+
+------
+
+*********
+Inference
+*********
+
+The :class:`~flash.vision.ImageClassifier` is already pre-trained on `ImageNet <http://www.image-net.org/>`_, a dataset of over 14 million images.
+
+We can use the :class:`~flash.vision.ImageClassifier` model (pretrained on our data) for inference on any string sequence using :func:`~flash.vision.ImageClassifier.predict`.
+We can also add a simple visualisation by extending :class:`~flash.data.base_viz.BaseVisualization`, like this:
+
+.. code-block:: python
+
+    # import our libraries
+    from typing import Any
+
+    import torchvision.transforms.functional as T
+    from torchvision.utils import make_grid
+
+    from flash import Trainer
+    from flash.data.base_viz import BaseVisualization
+    from flash.data.utils import download_data
+    from flash.vision import ImageClassificationData, ImageClassifier
+
+    # 1. Download the data
+    download_data("https://pl-flash-data.s3.amazonaws.com/movie_posters.zip", "data/")
+
+    # 2. Define our custom visualisation and datamodule
+    class CustomViz(BaseVisualization):
+
+        def show_per_batch_transform(self, batch: Any, _):
+            images = batch[0]
+            image = make_grid(images, nrow=2)
+            image = T.to_pil_image(image, 'RGB')
+            image.show()
+
+
+    # 3. Load the model from a checkpoint
+    model = ImageClassifier.load_from_checkpoint(
+        "https://flash-weights.s3.amazonaws.com/image_classification_multi_label_model.pt",
+    )
+
+    # 4a. Predict the genres of a few movie posters!
+    predictions = model.predict([
+        "data/movie_posters/val/tt0361500.jpg",
+        "data/movie_posters/val/tt0361748.jpg",
+        "data/movie_posters/val/tt0362478.jpg",
+    ])
+    print(predictions)
+
+    # 4b. Or generate predictions with a whole folder!
+    datamodule = ImageClassificationData.from_folders(
+        predict_folder="data/movie_posters/predict/",
+        data_fetcher=CustomViz(),
+        preprocess=model.preprocess,
+    )
+
+    predictions = Trainer().predict(model, datamodule=datamodule)
+    print(predictions)
+
+    # 5. Show some data!
+    datamodule.show_predict_batch()
+
+For more advanced inference options, see :ref:`predictions`.
+
+------
+
+**********
+Finetuning
+**********
+
+Now let's look at how we can finetune a model on the movie poster data.
+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.ImageClassificationData`.
+
+.. note:: The dataset contains ``train`` and ``validation`` folders, and then each folder contains images and a ``metadata.csv`` which stores the labels.
+
+.. code-block::
+
+    movie_posters
+    ├── train
+    │   ├── metadata.csv
+    │   ├── tt0084058.jpg
+    │   ├── tt0084867.jpg
+    │   ...
+    └── val
+        ├── metadata.csv
+        ├── tt0200465.jpg
+        ├── tt0326965.jpg
+        ...
+
+
+The ``metadata.csv`` files in each folder contain our labels, so we need to create a function (``load_data``) to extract the list of images and associated labels:
+
+.. code-block:: python
+
+    # import our libraries
+    import os
+    from typing import List, Tuple
+
+    import pandas as pd
+    import torch
+
+    genres = [
+        "Action", "Adventure", "Animation", "Biography", "Comedy", "Crime", "Documentary", "Drama", "Family", "Fantasy", "History", "Horror", "Music", "Musical", "Mystery", "N/A", "News", "Reality-TV", "Romance", "Sci-Fi", "Short", "Sport", "Thriller", "War", "Western"
+    ]
+
+    def load_data(data: str, root: str = 'data/movie_posters') -> Tuple[List[str], List[List[int]]]:
+        metadata = pd.read_csv(os.path.join(root, data, "metadata.csv"))
+
+        images = []
+        labels = []
+        for _, row in metadata.iterrows():
+            images.append(os.path.join(root, data, row['Id'] + ".jpg"))
+            labels.append([int(row[genre]) for genre in genres])
+
+        return images, labels
+
+Our :class:`~flash.data.process.Preprocess` overrides the :meth:`~flash.data.process.Preprocess.load_data` method to create an iterable of image paths and label tensors. The :class:`~flash.vision.classification.data.ImageClassificationPreprocess` then handles loading and augmenting the images for us!
+Now all we need is three lines of code to build to train our task!
+
+.. note:: We need set `multi_label=True` in both our :class:`~flash.Task` and our :class:`~flash.data.process.Serializer` to use a binary cross entropy loss and to process outputs correctly.
+
+.. code-block:: python
+
+    import flash
+    from flash.core.classification import Labels
+    from flash.core.finetuning import FreezeUnfreeze
+    from flash.data.utils import download_data
+    from flash.vision import ImageClassificationData, ImageClassifier
+    from flash.vision.classification.data import ImageClassificationPreprocess
+
+    # 1. Download the data
+    download_data("https://pl-flash-data.s3.amazonaws.com/movie_posters.zip", "data/")
+
+    # 2. Load the data
+    ImageClassificationPreprocess.image_size = (128, 128)
+
+    train_filepaths, train_labels = load_data('train')
+    val_filepaths, val_labels = load_data('val')
+    test_filepaths, test_labels = load_data('test')
+
+    datamodule = ImageClassificationData.from_filepaths(
+        train_filepaths=train_filepaths,
+        train_labels=train_labels,
+        val_filepaths=val_filepaths,
+        val_labels=val_labels,
+        test_filepaths=test_filepaths,
+        test_labels=test_labels,
+        preprocess=ImageClassificationPreprocess(),
+    )
+
+    # 3. Build the model
+    model = ImageClassifier(
+        backbone="resnet18",
+        num_classes=len(genres),
+        multi_label=True,
+    )
+
+    # 4. Create the trainer.
+    trainer = flash.Trainer(max_epochs=1, limit_train_batches=1, limit_val_batches=1)
+
+    # 5. Train the model
+    trainer.finetune(model, datamodule=datamodule, strategy=FreezeUnfreeze(unfreeze_epoch=1))
+
+    # 6a. Predict what's on a few images!
+
+    # Serialize predictions as labels.
+    model.serializer = Labels(genres, multi_label=True)
+
+    predictions = model.predict([
+        "data/movie_posters/val/tt0361500.jpg",
+        "data/movie_posters/val/tt0361748.jpg",
+        "data/movie_posters/val/tt0362478.jpg",
+    ])
+
+    print(predictions)
+
+    datamodule = ImageClassificationData.from_folders(
+        predict_folder="data/movie_posters/predict/",
+        preprocess=model.preprocess,
+    )
+
+    # 6b. Or generate predictions with a whole folder!
+    predictions = trainer.predict(model, datamodule=datamodule)
+    print(predictions)
+
+    # 7. Save it!
+    trainer.save_checkpoint("image_classification_multi_label_model.pt")
+
+------
+
+For more backbone options, see :ref:`image_classification`.