def add_progress_bar(trainer, evaluator, validation_loader, epoch_length):
"""
"I can't believe it's not Keras"
Running average accuracy and loss metrics + TQDM progressbar
"""
training_history = {'accuracy': [], 'loss': []}
validation_history = {'accuracy': [], 'loss': []}
last_epoch = []
RunningAverage(output_transform=lambda x: x[0]).attach(trainer, 'loss')
RunningAverage(Accuracy(output_transform=lambda x: (x[1], x[2]))).attach(
trainer, 'accuracy')
prog_bar = ProgressBar()
prog_bar.attach(trainer, ['loss', 'accuracy'])
prog_bar.pbar_cls = tqdm.tqdm
prog_bar_vd = ProgressBar()
prog_bar_vd.attach(evaluator)
prog_bar_vd.pbar_cls = tqdm.tqdm
from ignite.handlers import Timer
timer = Timer(average=True)
timer.attach(trainer,
start=Events.EPOCH_STARTED,
resume=Events.EPOCH_STARTED,
pause=Events.EPOCH_COMPLETED,
step=Events.EPOCH_COMPLETED)
@trainer.on(Events.EPOCH_COMPLETED)
def log_validation_results(trainer):
metrics = trainer.state.metrics
accuracy = metrics['accuracy'] * 100
loss = metrics['nll']
last_epoch.append(0)
training_history['accuracy'].append(accuracy)
training_history['loss'].append(loss)
train_msg = "Train Epoch {}: acc: {:.2f}% loss: {:.2f}, train time: {:.2f}s".format(
trainer.state.epoch, accuracy, loss, timer.value())
evaluator.run(validation_loader, epoch_length=epoch_length)
metrics = evaluator.state.metrics
accuracy = metrics['accuracy'] * 100
loss = metrics['nll']
validation_history['accuracy'].append(accuracy)
validation_history['loss'].append(loss)
val_msg = "Valid Epoch {}: acc: {:.2f}% loss: {:.2f}".format(
trainer.state.epoch, accuracy, loss)
prog_bar_vd.log_message(train_msg + " --- " + val_msg)
