kfoldmultitask.py

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 torchmetrics.classification.accuracy import Accuracy

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                  #
#############################################################################################


#############################################################################################
#                           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 split 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)

    def __post_init__(cls):
        super().__init__()


#############################################################################################
#                           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]) -> None:
        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])
        self.test_acc = Accuracy()

    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.nll_loss(logits, batch[1])
        self.test_acc(logits, batch[1])
        self.log("test_acc", self.test_acc)
        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, export_path: str) -> None:
        super().__init__()
        self.num_folds = num_folds
        self.current_fold: int = 0
        self.export_path = export_path

    @property
    def done(self) -> bool:
        return self.current_fold >= self.num_folds

    def connect(self, fit_loop: FitLoop) -> None:
        self.fit_loop = fit_loop

    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.strategy.setup_optimizers(self.trainer)
        self.replace(fit_loop=FitLoop)

    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.strategy.connect(voting_model)
        self.trainer.strategy.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]


class LitImageClassifier(ImageClassifier):
    def __init__(self) -> None:
        super().__init__()
        self.val_acc = Accuracy()

    def validation_step(self, batch: Any, batch_idx: int) -> None:
        x, y = batch
        logits = self.forward(x)
        loss = F.nll_loss(logits, y.long())
        self.val_acc(logits, y)
        self.log("val_acc", self.val_acc)
        self.log("val_loss", loss)


#############################################################################################
#                           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.                        #
#############################################################################################

if __name__ == "__main__":
    seed_everything(42)
    model = LitImageClassifier()
    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=2,
        accelerator="auto",
        strategy="ddp",
    )
    internal_fit_loop = trainer.fit_loop
    trainer.fit_loop = KFoldLoop(5, export_path="./")
    trainer.fit_loop.connect(internal_fit_loop)
    trainer.fit(model, datamodule)