282 lines
13 KiB
Python
282 lines
13 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os.path as osp
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from abc import ABC, abstractmethod
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from copy import deepcopy
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Type
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import torch
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import torchvision.transforms as T
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from sklearn.model_selection import KFold
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from torch.nn import functional as F
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from torch.utils.data import random_split
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from torch.utils.data.dataloader import DataLoader
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from torch.utils.data.dataset import Dataset, Subset
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from torchmetrics.classification.accuracy import Accuracy
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from pl_examples import _DATASETS_PATH
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from pl_examples.basic_examples.mnist_datamodule import MNIST
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from pl_examples.basic_examples.mnist_examples.image_classifier_4_lightning_module import ImageClassifier
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from pytorch_lightning import LightningDataModule, seed_everything, Trainer
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.loops.base import Loop
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from pytorch_lightning.loops.fit_loop import FitLoop
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from pytorch_lightning.trainer.states import TrainerFn
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#############################################################################################
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# KFold Loop / Cross Validation Example #
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# This example demonstrates how to leverage Lightning Loop Customization introduced in v1.5 #
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# Learn more about the loop structure from the documentation: #
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# https://pytorch-lightning.readthedocs.io/en/latest/extensions/loops.html #
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#############################################################################################
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#############################################################################################
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# Step 1 / 5: Define KFold DataModule API #
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# Our KFold DataModule requires to implement the `setup_folds` and `setup_fold_index` #
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# methods. #
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#############################################################################################
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class BaseKFoldDataModule(LightningDataModule, ABC):
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@abstractmethod
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def setup_folds(self, num_folds: int) -> None:
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pass
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@abstractmethod
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def setup_fold_index(self, fold_index: int) -> None:
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pass
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#############################################################################################
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# Step 2 / 5: Implement the KFoldDataModule #
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# The `KFoldDataModule` will take a train and test dataset. #
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# On `setup_folds`, folds will be created depending on the provided argument `num_folds` #
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# Our `setup_fold_index`, the provided train dataset will be split accordingly to #
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# the current fold split. #
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#############################################################################################
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@dataclass
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class MNISTKFoldDataModule(BaseKFoldDataModule):
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train_dataset: Optional[Dataset] = None
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test_dataset: Optional[Dataset] = None
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train_fold: Optional[Dataset] = None
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val_fold: Optional[Dataset] = None
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def prepare_data(self) -> None:
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# download the data.
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MNIST(_DATASETS_PATH, transform=T.Compose([T.ToTensor(), T.Normalize(mean=(0.5,), std=(0.5,))]))
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def setup(self, stage: Optional[str] = None) -> None:
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# load the data
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dataset = MNIST(_DATASETS_PATH, transform=T.Compose([T.ToTensor(), T.Normalize(mean=(0.5,), std=(0.5,))]))
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self.train_dataset, self.test_dataset = random_split(dataset, [50000, 10000])
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def setup_folds(self, num_folds: int) -> None:
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self.num_folds = num_folds
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self.splits = [split for split in KFold(num_folds).split(range(len(self.train_dataset)))]
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def setup_fold_index(self, fold_index: int) -> None:
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train_indices, val_indices = self.splits[fold_index]
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self.train_fold = Subset(self.train_dataset, train_indices)
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self.val_fold = Subset(self.train_dataset, val_indices)
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def train_dataloader(self) -> DataLoader:
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return DataLoader(self.train_fold)
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def val_dataloader(self) -> DataLoader:
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return DataLoader(self.val_fold)
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def test_dataloader(self) -> DataLoader:
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return DataLoader(self.test_dataset)
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def __post_init__(cls):
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super().__init__()
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#############################################################################################
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# Step 3 / 5: Implement the EnsembleVotingModel module #
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# The `EnsembleVotingModel` will take our custom LightningModule and #
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# several checkpoint_paths. #
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# #
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#############################################################################################
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class EnsembleVotingModel(LightningModule):
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def __init__(self, model_cls: Type[LightningModule], checkpoint_paths: List[str]) -> None:
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super().__init__()
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# Create `num_folds` models with their associated fold weights
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self.models = torch.nn.ModuleList([model_cls.load_from_checkpoint(p) for p in checkpoint_paths])
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self.test_acc = Accuracy()
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def test_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> None:
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# Compute the averaged predictions over the `num_folds` models.
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logits = torch.stack([m(batch[0]) for m in self.models]).mean(0)
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loss = F.nll_loss(logits, batch[1])
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self.test_acc(logits, batch[1])
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self.log("test_acc", self.test_acc)
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self.log("test_loss", loss)
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#############################################################################################
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# Step 4 / 5: Implement the KFoldLoop #
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# From Lightning v1.5, it is possible to implement your own loop. There is several steps #
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# to do so which are described in detail within the documentation #
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# https://pytorch-lightning.readthedocs.io/en/latest/extensions/loops.html. #
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# Here, we will implement an outer fit_loop. It means we will implement subclass the #
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# base Loop and wrap the current trainer `fit_loop`. #
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#############################################################################################
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#############################################################################################
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# Here is the `Pseudo Code` for the base Loop. #
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# class Loop: #
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# #
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# def run(self, ...): #
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# self.reset(...) #
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# self.on_run_start(...) #
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# #
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# while not self.done: #
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# self.on_advance_start(...) #
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# self.advance(...) #
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# self.on_advance_end(...) #
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# #
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# return self.on_run_end(...) #
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#############################################################################################
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class KFoldLoop(Loop):
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def __init__(self, num_folds: int, export_path: str) -> None:
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super().__init__()
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self.num_folds = num_folds
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self.current_fold: int = 0
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self.export_path = export_path
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@property
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def done(self) -> bool:
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return self.current_fold >= self.num_folds
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def connect(self, fit_loop: FitLoop) -> None:
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self.fit_loop = fit_loop
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def reset(self) -> None:
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"""Nothing to reset in this loop."""
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def on_run_start(self, *args: Any, **kwargs: Any) -> None:
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"""Used to call `setup_folds` from the `BaseKFoldDataModule` instance and store the original weights of the
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model."""
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assert isinstance(self.trainer.datamodule, BaseKFoldDataModule)
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self.trainer.datamodule.setup_folds(self.num_folds)
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self.lightning_module_state_dict = deepcopy(self.trainer.lightning_module.state_dict())
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def on_advance_start(self, *args: Any, **kwargs: Any) -> None:
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"""Used to call `setup_fold_index` from the `BaseKFoldDataModule` instance."""
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print(f"STARTING FOLD {self.current_fold}")
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assert isinstance(self.trainer.datamodule, BaseKFoldDataModule)
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self.trainer.datamodule.setup_fold_index(self.current_fold)
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def advance(self, *args: Any, **kwargs: Any) -> None:
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"""Used to the run a fitting and testing on the current hold."""
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self._reset_fitting() # requires to reset the tracking stage.
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self.fit_loop.run()
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self._reset_testing() # requires to reset the tracking stage.
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self.trainer.test_loop.run()
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self.current_fold += 1 # increment fold tracking number.
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def on_advance_end(self) -> None:
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"""Used to save the weights of the current fold and reset the LightningModule and its optimizers."""
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self.trainer.save_checkpoint(osp.join(self.export_path, f"model.{self.current_fold}.pt"))
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# restore the original weights + optimizers and schedulers.
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self.trainer.lightning_module.load_state_dict(self.lightning_module_state_dict)
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self.trainer.strategy.setup_optimizers(self.trainer)
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self.replace(fit_loop=FitLoop)
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def on_run_end(self) -> None:
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"""Used to compute the performance of the ensemble model on the test set."""
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checkpoint_paths = [osp.join(self.export_path, f"model.{f_idx + 1}.pt") for f_idx in range(self.num_folds)]
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voting_model = EnsembleVotingModel(type(self.trainer.lightning_module), checkpoint_paths)
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voting_model.trainer = self.trainer
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# This requires to connect the new model and move it the right device.
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self.trainer.strategy.connect(voting_model)
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self.trainer.strategy.model_to_device()
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self.trainer.test_loop.run()
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def on_save_checkpoint(self) -> Dict[str, int]:
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return {"current_fold": self.current_fold}
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def on_load_checkpoint(self, state_dict: Dict) -> None:
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self.current_fold = state_dict["current_fold"]
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def _reset_fitting(self) -> None:
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self.trainer.reset_train_dataloader()
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self.trainer.reset_val_dataloader()
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self.trainer.state.fn = TrainerFn.FITTING
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self.trainer.training = True
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def _reset_testing(self) -> None:
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self.trainer.reset_test_dataloader()
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self.trainer.state.fn = TrainerFn.TESTING
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self.trainer.testing = True
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def __getattr__(self, key) -> Any:
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# requires to be overridden as attributes of the wrapped loop are being accessed.
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if key not in self.__dict__:
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return getattr(self.fit_loop, key)
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return self.__dict__[key]
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class LitImageClassifier(ImageClassifier):
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def __init__(self) -> None:
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super().__init__()
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self.val_acc = Accuracy()
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def validation_step(self, batch: Any, batch_idx: int) -> None:
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x, y = batch
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logits = self.forward(x)
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loss = F.nll_loss(logits, y.long())
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self.val_acc(logits, y)
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self.log("val_acc", self.val_acc)
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self.log("val_loss", loss)
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#############################################################################################
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# Step 5 / 5: Connect the KFoldLoop to the Trainer #
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# After creating the `KFoldDataModule` and our model, the `KFoldLoop` is being connected to #
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# the Trainer. #
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# Finally, use `trainer.fit` to start the cross validation training. #
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#############################################################################################
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if __name__ == "__main__":
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seed_everything(42)
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model = LitImageClassifier()
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datamodule = MNISTKFoldDataModule()
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trainer = Trainer(
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max_epochs=10,
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limit_train_batches=2,
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limit_val_batches=2,
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limit_test_batches=2,
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num_sanity_val_steps=0,
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devices=2,
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accelerator="auto",
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strategy="ddp",
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)
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internal_fit_loop = trainer.fit_loop
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trainer.fit_loop = KFoldLoop(5, export_path="./")
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trainer.fit_loop.connect(internal_fit_loop)
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trainer.fit(model, datamodule)
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