pytorch_ignite_simple.py

"""
Optuna example that optimizes convolutional neural networks using PyTorch Ignite.

In this example, we optimize the validation accuracy of hand-written digit recognition using
PyTorch Ignite and FashionMNIST. We optimize the neural network architecture as well as the
regularization. As it is too time consuming to use the whole FashionMNIST dataset,
we here use a small subset of it.

You can run this example as follows, pruning can be turned on and off with the `--pruning`
argument.
    $ python pytorch_ignite_simple.py [--pruning]

"""

import argparse

from ignite.engine import create_supervised_evaluator
from ignite.engine import create_supervised_trainer
from ignite.engine import Events
from ignite.metrics import Accuracy
import optuna
import torch
from torch import nn
import torch.nn.functional as F
from torch.optim import Adam
from torch.utils.data import DataLoader
from torch.utils.data import Subset
from torchvision.datasets.mnist import FashionMNIST
from torchvision.transforms import Compose
from torchvision.transforms import Normalize
from torchvision.transforms import ToTensor


EPOCHS = 10
TRAIN_BATCH_SIZE = 64
VAL_BATCH_SIZE = 1000
N_TRAIN_EXAMPLES = 3000
N_VALID_EXAMPLES = 1000


class Net(nn.Module):
    def __init__(self, trial):
        # We optimize dropout rate in a convolutional neural network.
        super().__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        dropout_rate = trial.suggest_float("dropout_rate", 0, 1)
        self.conv2_drop = nn.Dropout2d(p=dropout_rate)
        fc2_input_dim = trial.suggest_int("fc2_input_dim", 40, 80)
        self.fc1 = nn.Linear(320, fc2_input_dim)
        self.fc2 = nn.Linear(fc2_input_dim, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=-1)


def get_data_loaders(train_batch_size, val_batch_size):
    data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])

    train_data = FashionMNIST(download=True, root=".", transform=data_transform, train=True)
    val_data = FashionMNIST(download=False, root=".", transform=data_transform, train=False)

    train_loader = DataLoader(
        Subset(train_data, range(N_TRAIN_EXAMPLES)), batch_size=train_batch_size, shuffle=True
    )
    val_loader = DataLoader(
        Subset(val_data, range(N_VALID_EXAMPLES)), batch_size=val_batch_size, shuffle=False
    )

    return train_loader, val_loader


def objective(trial):
    # Create a convolutional neural network.
    model = Net(trial)

    device = "cpu"
    if torch.cuda.is_available():
        device = "cuda"
        model.cuda(device)

    optimizer = Adam(model.parameters())
    trainer = create_supervised_trainer(model, optimizer, F.nll_loss, device=device)
    evaluator = create_supervised_evaluator(model, metrics={"accuracy": Accuracy()}, device=device)

    # Register a pruning handler to the evaluator.
    pruning_handler = optuna.integration.PyTorchIgnitePruningHandler(trial, "accuracy", trainer)
    evaluator.add_event_handler(Events.COMPLETED, pruning_handler)

    # Load FashionMNIST dataset.
    train_loader, val_loader = get_data_loaders(TRAIN_BATCH_SIZE, VAL_BATCH_SIZE)

    @trainer.on(Events.EPOCH_COMPLETED)
    def log_results(engine):
        evaluator.run(val_loader)
        validation_acc = evaluator.state.metrics["accuracy"]
        print("Epoch: {} Validation accuracy: {:.2f}".format(engine.state.epoch, validation_acc))

    trainer.run(train_loader, max_epochs=EPOCHS)

    evaluator.run(val_loader)
    return evaluator.state.metrics["accuracy"]


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="PyTorch Ignite example.")
    parser.add_argument(
        "--pruning",
        "-p",
        action="store_true",
        help="Activate the pruning feature. `MedianPruner` stops unpromising "
        "trials at the early stages of training.",
    )
    args = parser.parse_args()
    pruner = optuna.pruners.MedianPruner() if args.pruning else optuna.pruners.NopPruner()

    study = optuna.create_study(direction="maximize", pruner=pruner)
    study.optimize(objective, n_trials=100, timeout=600)

    print("Number of finished trials: ", len(study.trials))

    print("Best trial:")
    trial = study.best_trial

    print("  Value: ", trial.value)

    print("  Params: ")
    for key, value in trial.params.items():
        print("    {}: {}".format(key, value))