Fix tqdm serialization

[Yuyan-Li]

This removes the TQDM bar from the serialization.

It prevents the error when saving the trainer: TypeError: cannot serialize '_io.TextIOWrapper' object

I think the bar will be rebuilt automatically (haven't tested it yet).

[DerThorsten]

anyone an opinion on merging this?

[nasimrahaman]

If the deserialisation works (which @Yuyan-Li has not tested yet), sure. @DerThorsten would you have time to do a quick test?

[DerThorsten]

Let's see if @Yuyan-Li whants to contribute a test, if not I'll write one soonish

[Yuyan-Li]

I can test it on my system but I don't know how to write proper unittests. I could write a sample script showing that it works if that's enough.

[Yuyan-Li]

So I tested it and the deserialisation works. I also fixed it so that it shows the proper epoch in the bar when continuing the training. But there seems to be a problem with continuing the training after loading a checkpoint. My script crashes at the end with an error (ConnectionRefusedError: [Errno 111] Connection refused) in the dataloader. This should be unrelated to the TQDM bar because it also happens without it. I put the script I used below (it's heavily borrowd from tests/test_training/test_basic.py). Maybe someone has time to take a look.

def _make_test_model():
    import torch.nn as nn
    from inferno.extensions.layers.reshape import AsMatrix

    toy_net = nn.Sequential(nn.Conv2d(3, 8, 3, 1, 1),
                            nn.ELU(),
                            nn.MaxPool2d(2),
                            nn.Conv2d(8, 8, 3, 1, 1),
                            nn.ELU(),
                            nn.MaxPool2d(2),
                            nn.Conv2d(8, 16, 3, 1, 1),
                            nn.ELU(),
                            nn.AdaptiveAvgPool2d((1, 1)),
                            AsMatrix(),
                            nn.Linear(16, 10))
    return toy_net

def test_serialization():
    from inferno.trainers.basic import Trainer
    from inferno.trainers.callbacks import TQDMProgressBar
    from inferno.io.box.cifar import get_cifar10_loaders
    # Make model
    net = _make_test_model()
    # Make trainer
    trainer = Trainer(model=net) \
        .build_optimizer('Adam') \
        .build_criterion('CrossEntropyLoss') \
        .build_metric('CategoricalError') \
        .validate_every((1, 'epochs')) \
        .save_every((1, 'epochs'), to_directory='saves') \
        .set_max_num_iterations(500) \
        .register_callback(TQDMProgressBar())

    train_loader, validate_loader = get_cifar10_loaders(root_directory='.', download=True)
    trainer.bind_loader('train', train_loader)
    trainer.bind_loader('validate', validate_loader)

    # Try to train
    trainer.fit()
    # Try to serialize
    trainer.save()

def test_deserialization():
    from inferno.trainers.basic import Trainer
    from inferno.io.box.cifar import get_cifar10_loaders

    net = _make_test_model()
    # Try to unserialize
    trainer = Trainer(net).save_to_directory('saves').load()

    train_loader, validate_loader = get_cifar10_loaders(root_directory='.', download=True)
    trainer.bind_loader('train', train_loader)
    trainer.bind_loader('validate', validate_loader)

    # Try to continue training
    trainer.set_max_num_iterations(800)
    trainer.fit()

if __name__=='__main__':
    test_serialization()
    test_deserialization()

[svenpeter42]

The "training epoch x" bar isn't restored correctly. If you set trainer.set_max_num_iterations(800) to something larger you will notice

Training epoch 1: : 500it [00:16, 29.77it/s] | 2/1000 [00:16<2:20:35, 8.45s/it]

even though each epoch only has 391 iterations.