Add KFold Loop example

CHANGELOG.md

@@ -186,14 +186,19 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added support for `torch.autograd.set_detect_anomaly` through `Trainer` constructor argument `detect_anomaly` ([#9848](https://github.com/PyTorchLightning/pytorch-lightning/pull/9848))
 
+
 - Added a `len` method to `LightningDataModule` ([#9895](https://github.com/PyTorchLightning/pytorch-lightning/pull/9895))
 
+
 - Added `enable_model_summary` flag to Trainer ([#9699](https://github.com/PyTorchLightning/pytorch-lightning/pull/9699))
 
 
 - Added `strategy` argument to Trainer ([#8597](https://github.com/PyTorchLightning/pytorch-lightning/pull/8597))
 
 
+- Added `kfold` example for loop customization ([#9965](https://github.com/PyTorchLightning/pytorch-lightning/pull/9965))
+
+
 - LightningLite:
     * Added `PrecisionPlugin.forward_context`, making it the default implementation for all `{train,val,test,predict}_step_context()` methods ([#9988](https://github.com/PyTorchLightning/pytorch-lightning/pull/9988))
 

docs/source/extensions/loops.rst

@@ -395,7 +395,17 @@ To run the following demo, install Flash and `BaaL <https://github.com/ElementAI
     # 5. Save the model!
     trainer.save_checkpoint("image_classification_model.pt")
 
-Here is the `runnable example <https://github.com/PyTorchLightning/lightning-flash/blob/master/flash_examples/integrations/baal/image_classification_active_learning.py>`_ and the `code for the active learning loop <https://github.com/PyTorchLightning/lightning-flash/blob/master/flash/image/classification/integrations/baal/loop.py#L31>`_.
+Here is the `Active Learning Loop example <https://github.com/PyTorchLightning/lightning-flash/blob/master/flash_examples/integrations/baal/image_classification_active_learning.py>`_ and the `code for the active learning loop <https://github.com/PyTorchLightning/lightning-flash/blob/master/flash/image/classification/integrations/baal/loop.py#L31>`_.
+
+
+`KFold / Cross Validation <https://en.wikipedia.org/wiki/Cross-validation_(statistics)>`__ is a machine learning practice in which the training dataset is being partitioned into `num_folds` complementary subsets.
+One cross validation round will perform fitting where one fold is left out for validation and the other folds are used for training.
+To reduce variability, once all rounds are performed using the different folds, the trained models are ensembled and their predictions are
+averaged when estimating the model's predictive performance on the test dataset.
+KFold can elegantly be implemented with `Lightning Loop Customization` as follows:
+
+Here is the `KFold Loop example <https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/loops/kfold.py>`_.
+
 
 Advanced Topics and Examples
 ----------------------------

pl_examples/basic_examples/mnist_datamodule.py

@@ -26,18 +26,21 @@
 if _TORCHVISION_AVAILABLE:
     from torchvision import transforms as transform_lib
 
-_TORCHVISION_MNIST_AVAILABLE = not bool(os.getenv("PL_USE_MOCKED_MNIST", False))
-if _TORCHVISION_MNIST_AVAILABLE:
-    try:
-        from torchvision.datasets import MNIST
-
-        MNIST(_DATASETS_PATH, download=True)
-    except HTTPError as e:
-        print(f"Error {e} downloading `torchvision.datasets.MNIST`")
-        _TORCHVISION_MNIST_AVAILABLE = False
-if not _TORCHVISION_MNIST_AVAILABLE:
-    print("`torchvision.datasets.MNIST` not available. Using our hosted version")
-    from tests.helpers.datasets import MNIST
+
+def MNIST(*args, **kwargs):
+    torchvision_mnist_available = not bool(os.getenv("PL_USE_MOCKED_MNIST", False))
+    if torchvision_mnist_available:
+        try:
+            from torchvision.datasets import MNIST
+
+            MNIST(_DATASETS_PATH, download=True)
+        except HTTPError as e:
+            print(f"Error {e} downloading `torchvision.datasets.MNIST`")
+            torchvision_mnist_available = False
+    if not torchvision_mnist_available:
+        print("`torchvision.datasets.MNIST` not available. Using our hosted version")
+        from tests.helpers.datasets import MNIST
+    return MNIST(*args, **kwargs)
 
 
 class MNISTDataModule(LightningDataModule):

pl_examples/loop_examples/kfold.py

@@ -0,0 +1,256 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.path as osp
+from abc import ABC, abstractmethod
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Type
+
+import torch
+import torchvision.transforms as T
+from sklearn.model_selection import KFold
+from torch.nn import functional as F
+from torch.utils.data import random_split
+from torch.utils.data.dataloader import DataLoader
+from torch.utils.data.dataset import Dataset, Subset
+
+from pl_examples import _DATASETS_PATH
+from pl_examples.basic_examples.mnist_datamodule import MNIST
+from pl_examples.basic_examples.simple_image_classifier import LitClassifier
+from pytorch_lightning import LightningDataModule, seed_everything, Trainer
+from pytorch_lightning.core.lightning import LightningModule
+from pytorch_lightning.loops.base import Loop
+from pytorch_lightning.loops.fit_loop import FitLoop
+from pytorch_lightning.trainer.states import TrainerFn
+
+#############################################################################################
+#                           KFold Loop / Cross Validation Example                           #
+# This example demonstrates how to leverage Lightning Loop Customization introduced in v1.5 #
+# Learn more about the loop structure from the documentation:                               #
+# https://pytorch-lightning.readthedocs.io/en/latest/extensions/loops.html                  #
+#############################################################################################
+
+
+seed_everything(42)
+
+
+#############################################################################################
+#                           Step 1 / 5: Define KFold DataModule API                         #
+# Our KFold DataModule requires to implement the `setup_folds` and `setup_fold_index`       #
+# methods.                                                                                  #
+#############################################################################################
+
+
+class BaseKFoldDataModule(LightningDataModule, ABC):
+    @abstractmethod
+    def setup_folds(self, num_folds: int) -> None:
+        pass
+
+    @abstractmethod
+    def setup_fold_index(self, fold_index: int) -> None:
+        pass
+
+
+#############################################################################################
+#                           Step 2 / 5: Implement the KFoldDataModule                       #
+# The `KFoldDataModule` will take a train and test dataset.                                 #
+# On `setup_folds`, folds will be created depending on the provided argument `num_folds`    #
+# Our `setup_fold_index`, the provided train dataset will be splitted accordingly to        #
+# the current fold split.                                                                   #
+#############################################################################################
+
+
+@dataclass
+class MNISTKFoldDataModule(BaseKFoldDataModule):
+
+    train_dataset: Optional[Dataset] = None
+    test_dataset: Optional[Dataset] = None
+    train_fold: Optional[Dataset] = None
+    val_fold: Optional[Dataset] = None
+
+    def prepare_data(self) -> None:
+        # download the data.
+        MNIST(_DATASETS_PATH, transform=T.Compose([T.ToTensor(), T.Normalize(mean=(0.5,), std=(0.5,))]))
+
+    def setup(self, stage: Optional[str] = None) -> None:
+        # load the data
+        dataset = MNIST(_DATASETS_PATH, transform=T.Compose([T.ToTensor(), T.Normalize(mean=(0.5,), std=(0.5,))]))
+        self.train_dataset, self.test_dataset = random_split(dataset, [50000, 10000])
+
+    def setup_folds(self, num_folds: int) -> None:
+        self.num_folds = num_folds
+        self.splits = [split for split in KFold(num_folds).split(range(len(self.train_dataset)))]
+
+    def setup_fold_index(self, fold_index: int) -> None:
+        train_indices, val_indices = self.splits[fold_index]
+        self.train_fold = Subset(self.train_dataset, train_indices)
+        self.val_fold = Subset(self.train_dataset, val_indices)
+
+    def train_dataloader(self) -> DataLoader:
+        return DataLoader(self.train_fold)
+
+    def val_dataloader(self) -> DataLoader:
+        return DataLoader(self.val_fold)
+
+    def test_dataloader(self) -> DataLoader:
+        return DataLoader(self.test_dataset)
+
+
+#############################################################################################
+#                           Step 3 / 5: Implement the EnsembleVotingModel module            #
+# The `EnsembleVotingModel` will take our custom LightningModule and                        #
+# several checkpoint_paths.                                                                 #
+#                                                                                           #
+#############################################################################################
+
+
+class EnsembleVotingModel(LightningModule):
+    def __init__(self, model_cls: Type[LightningModule], checkpoint_paths: List[str]):
+        super().__init__()
+        # Create `num_folds` models with their associated fold weights
+        self.models = torch.nn.ModuleList([model_cls.load_from_checkpoint(p) for p in checkpoint_paths])
+
+    def test_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> None:
+        # Compute the averaged predictions over the `num_folds` models.
+        logits = torch.stack([m(batch[0]) for m in self.models]).mean(0)
+        loss = F.cross_entropy(logits, batch[1])
+        self.log("test_loss", loss)
+
+
+#############################################################################################
+#                           Step 4 / 5: Implement the  KFoldLoop                            #
+# From Lightning v1.5, it is possible to implement your own loop. There is several steps    #
+# to do so which are described in detail within the documentation                           #
+# https://pytorch-lightning.readthedocs.io/en/latest/extensions/loops.html.                 #
+# Here, we will implement an outer fit_loop. It means we will implement subclass the        #
+# base Loop and wrap the current trainer `fit_loop`.                                        #
+#############################################################################################
+
+
+#############################################################################################
+#                     Here is the `Pseudo Code` for the base Loop.                          #
+# class Loop:                                                                               #
+#                                                                                           #
+#   def run(self, ...):                                                                     #
+#       self.reset(...)                                                                     #
+#       self.on_run_start(...)                                                              #
+#                                                                                           #
+#        while not self.done:                                                               #
+#            self.on_advance_start(...)                                                     #
+#            self.advance(...)                                                              #
+#            self.on_advance_end(...)                                                       #
+#                                                                                           #
+#        return self.on_run_end(...)                                                        #
+#############################################################################################
+
+
+class KFoldLoop(Loop):
+    def __init__(self, num_folds: int, fit_loop: FitLoop, export_path: str):
+        super().__init__()
+        self.num_folds = num_folds
+        self.fit_loop = fit_loop
+        self.current_fold: int = 0
+        self.export_path = export_path
+
+    @property
+    def done(self) -> bool:
+        return self.current_fold >= self.num_folds
+
+    def reset(self) -> None:
+        """Nothing to reset in this loop."""
+
+    def on_run_start(self, *args: Any, **kwargs: Any) -> None:
+        """Used to call `setup_folds` from the `BaseKFoldDataModule` instance and store the original weights of the
+        model."""
+        assert isinstance(self.trainer.datamodule, BaseKFoldDataModule)
+        self.trainer.datamodule.setup_folds(self.num_folds)
+        self.lightning_module_state_dict = deepcopy(self.trainer.lightning_module.state_dict())
+
+    def on_advance_start(self, *args: Any, **kwargs: Any) -> None:
+        """Used to call `setup_fold_index` from the `BaseKFoldDataModule` instance."""
+        print(f"STARTING FOLD {self.current_fold}")
+        assert isinstance(self.trainer.datamodule, BaseKFoldDataModule)
+        self.trainer.datamodule.setup_fold_index(self.current_fold)
+
+    def advance(self, *args: Any, **kwargs: Any) -> None:
+        """Used to the run a fitting and testing on the current hold."""
+        self._reset_fitting()  # requires to reset the tracking stage.
+        self.fit_loop.run()
+
+        self._reset_testing()  # requires to reset the tracking stage.
+        self.trainer.test_loop.run()
+        self.current_fold += 1  # increment fold tracking number.
+
+    def on_advance_end(self) -> None:
+        """Used to save the weights of the current fold and reset the LightningModule and its optimizers."""
+        self.trainer.save_checkpoint(osp.join(self.export_path, f"model.{self.current_fold}.pt"))
+        # restore the original weights + optimizers and schedulers.
+        self.trainer.lightning_module.load_state_dict(self.lightning_module_state_dict)
+        self.trainer.accelerator.setup_optimizers(self.trainer)
+
+    def on_run_end(self) -> None:
+        """Used to compute the performance of the ensemble model on the test set."""
+        checkpoint_paths = [osp.join(self.export_path, f"model.{f_idx + 1}.pt") for f_idx in range(self.num_folds)]
+        voting_model = EnsembleVotingModel(type(self.trainer.lightning_module), checkpoint_paths)
+        voting_model.trainer = self.trainer
+        # This requires to connect the new model and move it the right device.
+        self.trainer.accelerator.connect(voting_model)
+        self.trainer.training_type_plugin.model_to_device()
+        self.trainer.test_loop.run()
+
+    def on_save_checkpoint(self) -> Dict[str, int]:
+        return {"current_fold": self.current_fold}
+
+    def on_load_checkpoint(self, state_dict: Dict) -> None:
+        self.current_fold = state_dict["current_fold"]
+
+    def _reset_fitting(self) -> None:
+        self.trainer.reset_train_dataloader()
+        self.trainer.reset_val_dataloader()
+        self.trainer.state.fn = TrainerFn.FITTING
+        self.trainer.training = True
+
+    def _reset_testing(self) -> None:
+        self.trainer.reset_test_dataloader()
+        self.trainer.state.fn = TrainerFn.TESTING
+        self.trainer.testing = True
+
+    def __getattr__(self, key) -> Any:
+        # requires to be overridden as attributes of the wrapped loop are being accessed.
+        if key not in self.__dict__:
+            return getattr(self.fit_loop, key)
+        return self.__dict__[key]
+
+
+#############################################################################################
+#                           Step 5 / 5: Connect the KFoldLoop to the Trainer                #
+# After creating the `KFoldDataModule` and our model, the `KFoldLoop` is being connected to #
+# the Trainer.                                                                              #
+# Finally, use `trainer.fit` to start the cross validation training.                        #
+#############################################################################################
+
+model = LitClassifier()
+datamodule = MNISTKFoldDataModule()
+trainer = Trainer(
+    max_epochs=10,
+    limit_train_batches=2,
+    limit_val_batches=2,
+    limit_test_batches=2,
+    num_sanity_val_steps=0,
+    devices=1,
+    accelerator="auto",
+    strategy="ddp",
+)
+trainer.fit_loop = KFoldLoop(5, trainer.fit_loop, export_path="./")
+trainer.fit(model, datamodule)

pytorch_lightning/trainer/trainer.py

@@ -1225,7 +1225,8 @@ def _run_train(self) -> None:
         # reload data when needed
         model = self.lightning_module
 
-        self.reset_train_val_dataloaders(model)
+        if isinstance(self.fit_loop, FitLoop):
+            self.reset_train_val_dataloaders(model)
 
         self.fit_loop.trainer = self
         with torch.autograd.set_detect_anomaly(self._detect_anomaly):