migrating_ignite.py

# The fastai DataLoader is a drop-in replacement for Pytorch's;
#   no code changes are required other than changing the import line
from fastai.data.load import DataLoader
import torch
from torch import nn
from torch.optim import SGD
import torch.nn.functional as F
from torchvision.transforms import Compose, ToTensor, Normalize
from torchvision.datasets import MNIST

from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
from ignite.metrics import Accuracy, Loss
from ignite.utils import setup_logger

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 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_loader = DataLoader(
        MNIST(download=True, root=".", transform=data_transform, train=True), batch_size=train_batch_size, shuffle=True)
    val_loader = DataLoader(
        MNIST(download=False, root=".", transform=data_transform, train=False), batch_size=val_batch_size, shuffle=False)
    return train_loader, val_loader

def run(train_batch_size, val_batch_size, epochs, lr, momentum, log_interval):
    train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size)
    model = Net()
    device = "cpu"
    if torch.cuda.is_available(): device = "cuda"

    model.to(device)  # Move model before creating optimizer
    optimizer = SGD(model.parameters(), lr=lr, momentum=momentum)
    criterion = nn.NLLLoss()
    trainer = create_supervised_trainer(model, optimizer, criterion, device=device)
    trainer.logger = setup_logger("trainer")

    val_metrics = {"accuracy": Accuracy(), "nll": Loss(criterion)}
    evaluator = create_supervised_evaluator(model, metrics=val_metrics, device=device)
    evaluator.logger = setup_logger("evaluator")

    desc = "ITERATION - loss: {:.2f}"
    pbar = tqdm(initial=0, leave=False, total=len(train_loader), desc=desc.format(0))

    @trainer.on(Events.ITERATION_COMPLETED(every=log_interval))
    def log_training_loss(engine):
        pbar.desc = desc.format(engine.state.output)
        pbar.update(log_interval)

    @trainer.on(Events.EPOCH_COMPLETED)
    def log_training_results(engine):
        pbar.refresh()
        evaluator.run(train_loader)
        metrics = evaluator.state.metrics
        avg_accuracy = metrics["accuracy"]
        avg_nll = metrics["nll"]
        tqdm.write(
            "Training Results - Epoch: {}  Avg accuracy: {:.2f} Avg loss: {:.2f}".format(
                engine.state.epoch, avg_accuracy, avg_nll))

    @trainer.on(Events.EPOCH_COMPLETED)
    def log_validation_results(engine):
        evaluator.run(val_loader)
        metrics = evaluator.state.metrics
        avg_accuracy = metrics["accuracy"]
        avg_nll = metrics["nll"]
        tqdm.write(
            "Validation Results - Epoch: {}  Avg accuracy: {:.2f} Avg loss: {:.2f}".format(
                engine.state.epoch, avg_accuracy, avg_nll))
        pbar.n = pbar.last_print_n = 0

    @trainer.on(Events.EPOCH_COMPLETED | Events.COMPLETED)
    def log_time(engine):
        tqdm.write(
            "{} took {} seconds".format(trainer.last_event_name.name, trainer.state.times[trainer.last_event_name.name]))

    trainer.run(train_loader, max_epochs=epochs)