Example of K-fold Cross Validation with `Fabric` (#16909)
Co-authored-by: Nikhil Shenoy <nikhilshenoy@dhcp-128-189-229-146.ubcsecure.wireless.ubc.ca> Co-authored-by: Nikhil Shenoy <nikhilshenoy@dhcp-128-189-231-235.ubcsecure.wireless.ubc.ca> Co-authored-by: Nikhil Shenoy <nikhilshenoy@dhcp-128-189-229-169.ubcsecure.wireless.ubc.ca> Co-authored-by: Nikhil Shenoy <nikhilshenoy@dhcp-128-189-230-40.ubcsecure.wireless.ubc.ca>
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## K-Fold Cross Validation
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This is an example of performing K-Fold cross validation supported with [Lightning Fabric](https://pytorch-lightning.readthedocs.io/en/stable/fabric/fabric.html). To learn more about cross validation, check out [this article](https://sebastianraschka.com/blog/2016/model-evaluation-selection-part3.html#introduction-to-k-fold-cross-validation).
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We use the MNIST dataset to train a simple CNN model. We create the k-fold cross validation splits using the `ModelSelection.KFold` [class](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html) in the `scikit-learn` library. Ensure that you have the `scikit-learn` library installed;
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```bash
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pip install scikit-learn
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```
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#### Run K-Fold Image Classification with Lightning Fabric
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This script shows you how to scale the pure PyTorch code to enable GPU and multi-GPU training using [Lightning Fabric](https://pytorch-lightning.readthedocs.io/en/stable/fabric/fabric.html).
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```bash
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# CPU
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lightning run model train_fabric.py
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# GPU (CUDA or M1 Mac)
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lightning run model train_fabric.py --accelerator=gpu
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# Multiple GPUs
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lightning run model train_fabric.py --accelerator=gpu --devices=4
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```
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### References
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- [KFold Model Selection](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html)
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- [K-Fold Cross Validation by Sebastian Rashcka](https://sebastianraschka.com/blog/2016/model-evaluation-selection-part3.html#introduction-to-k-fold-cross-validation)
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- [Cross Validation Wiki](<https://en.wikipedia.org/wiki/Cross-validation_(statistics)#k-fold_cross-validation>)
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# Copyright The Lightning AI 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 argparse
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from os import path
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import torchvision.transforms as T
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from sklearn import model_selection
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from torch.utils.data import DataLoader, SubsetRandomSampler
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from torchmetrics.classification import Accuracy
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from torchvision.datasets import MNIST
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from lightning.fabric import Fabric # import Fabric
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from lightning.fabric import seed_everything
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DATASETS_PATH = path.join(path.dirname(__file__), "..", "..", "..", "Datasets")
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class Net(nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.conv1 = nn.Conv2d(1, 32, 3, 1)
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self.conv2 = nn.Conv2d(32, 64, 3, 1)
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self.dropout1 = nn.Dropout(0.25)
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self.dropout2 = nn.Dropout(0.5)
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self.fc1 = nn.Linear(9216, 128)
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self.fc2 = nn.Linear(128, 10)
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def forward(self, x):
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x = self.conv1(x)
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x = F.relu(x)
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x = self.conv2(x)
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x = F.relu(x)
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x = F.max_pool2d(x, 2)
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x = self.dropout1(x)
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x = torch.flatten(x, 1)
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x = self.fc1(x)
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x = F.relu(x)
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x = self.dropout2(x)
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x = self.fc2(x)
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output = F.log_softmax(x, dim=1)
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return output
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def train_dataloader(model, data_loader, optimizer, fabric, epoch, hparams, fold):
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# TRAINING LOOP
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model.train()
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for batch_idx, (data, target) in enumerate(data_loader):
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# NOTE: no need to call `.to(device)` on the data, target
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optimizer.zero_grad()
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output = model(data)
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loss = F.nll_loss(output, target)
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fabric.backward(loss) # instead of loss.backward()
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optimizer.step()
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if (batch_idx == 0) or ((batch_idx + 1) % hparams.log_interval == 0):
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print(
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"Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
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epoch,
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batch_idx * len(data),
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len(data_loader.dataset),
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100.0 * batch_idx / len(data_loader),
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loss.item(),
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)
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)
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if hparams.dry_run:
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break
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def validate_dataloader(model, data_loader, fabric, hparams, fold, acc_metric):
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model.eval()
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loss = 0
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with torch.no_grad():
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for data, target in data_loader:
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# NOTE: no need to call `.to(device)` on the data, target
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output = model(data)
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loss += F.nll_loss(output, target, reduction="sum").item()
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# Accuracy with torchmetrics
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acc_metric.update(output, target)
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if hparams.dry_run:
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break
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# all_gather is used to aggregated the value across processes
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loss = fabric.all_gather(loss).sum() / len(data_loader.dataset)
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# compute acc
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acc = acc_metric.compute() * 100
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print(f"\nFor fold: {fold} Validation set: Average loss: {loss:.4f}, Accuracy: ({acc:.0f}%)\n")
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return acc
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def run(hparams):
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# Create the Lightning Fabric object. The parameters like accelerator, strategy, devices etc. will be proided
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# by the command line. See all options: `lightning run model --help`
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fabric = Fabric()
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seed_everything(hparams.seed) # instead of torch.manual_seed(...)
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transform = T.Compose([T.ToTensor(), T.Normalize((0.1307,), (0.3081,))])
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# This is meant to ensure the data are download only by 1 process.
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if fabric.is_global_zero:
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MNIST(DATASETS_PATH, download=True)
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fabric.barrier()
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# initialize dataset
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dataset = MNIST(DATASETS_PATH, train=True, transform=transform)
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# Loop over different folds (shuffle = False by default so reproducible)
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folds = hparams.folds
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kfold = model_selection.KFold(n_splits=folds)
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# initialize n_splits models and optimizers
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models = [Net() for _ in range(kfold.n_splits)]
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optimizers = [optim.Adadelta(model.parameters(), lr=hparams.lr) for model in models]
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# fabric setup for models and optimizers
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for i in range(kfold.n_splits):
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models[i], optimizers[i] = fabric.setup(models[i], optimizers[i])
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# Accuracy using torchmetrics
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acc_metric = Accuracy(task="multiclass", num_classes=10).to(fabric.device)
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# loop over epochs
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for epoch in range(1, hparams.epochs + 1):
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# loop over folds
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epoch_acc = 0
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for fold, (train_ids, val_ids) in enumerate(kfold.split(dataset)):
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print(f"Working on fold {fold}")
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# initialize dataloaders based on folds
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batch_size = hparams.batch_size
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train_loader = DataLoader(dataset, batch_size=batch_size, sampler=SubsetRandomSampler(train_ids))
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val_loader = DataLoader(dataset, batch_size=batch_size, sampler=SubsetRandomSampler(val_ids))
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# get model and optimizer for the current fold
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model, optimizer = models[fold], optimizers[fold]
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# train and validate
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train_dataloader(model, train_loader, optimizer, fabric, epoch, hparams, fold)
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epoch_acc += validate_dataloader(model, val_loader, fabric, hparams, fold, acc_metric)
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acc_metric.reset()
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# log epoch metrics
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print(f"Epoch {epoch} - Average acc: {epoch_acc / kfold.n_splits}")
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if hparams.dry_run:
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break
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# When using distributed training, use `fabric.save`
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# to ensure the current process is allowed to save a checkpoint
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if hparams.save_model:
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fabric.save(model.state_dict(), "mnist_cnn.pt")
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if __name__ == "__main__":
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# Arguments can be passed in through the CLI as normal and will be parsed here
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# Example:
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# lightning run model image_classifier.py accelerator=cuda --epochs=3
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parser = argparse.ArgumentParser(description="Fabric MNIST K-Fold Cross Validation Example")
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parser.add_argument(
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"--batch-size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
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)
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parser.add_argument("--epochs", type=int, default=14, metavar="N", help="number of epochs to train (default: 14)")
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parser.add_argument("--lr", type=float, default=1.0, metavar="LR", help="learning rate (default: 1.0)")
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parser.add_argument("--gamma", type=float, default=0.7, metavar="M", help="Learning rate step gamma (default: 0.7)")
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parser.add_argument("--dry-run", action="store_true", default=False, help="quickly check a single pass")
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parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)")
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parser.add_argument(
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"--log-interval",
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type=int,
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default=10,
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metavar="N",
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help="how many batches to wait before logging training status",
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)
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parser.add_argument("--folds", type=int, default=5, help="number of folds for k-fold cross validation")
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parser.add_argument("--save-model", action="store_true", default=False, help="For Saving the current Model")
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hparams = parser.parse_args()
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run(hparams)
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