Rethink usage pattern for pretrained models

🚀 Feature

Switch to using SomeModel.from_pretrained('pretrained-model-name') for pretrained models.

Motivation

Seems we are following torchvision’s pattern of having a ‘pretrained’ argument in the init of our models to initialize a pretrained model. In my opinion, this is extremely confusing as it makes the other init args + kwargs ambiguous/useless.

Pitch

add .from_pretrained classmethod to models and initialize an instance of the class based off of that. Pretrained models should incorporate any hparams needed to fill out init, I guess.

from pl_bolts.models import VAE

model = VAE.from_pretrained('imagenet2012')

Alternatives

Additional context

[williamFalcon]

oh i see. it’s an id not a dataset. yeah that works.

for instance we can have many backbones with different datasets as well

CPC.from_pretrained('resnet18-imagenet')
CPC.from_pretrained('resnet50-imagenet')
CPC.from_pretrained('resnet18-stl10')

[ananyahjha93]

@williamFalcon @Borda @nateraw I included this pattern in the latest AE, VAE commits to bolts. Few points that I realized:

1. We can shift the method from_pretrained() as a method to override in Lightning itself.
2. from_pretrained() needs to be an instance method and not a static method. In most cases, you will initialize the lightning module with specific params according the the weights being loaded.

vae = VAE(input_height=32, first_conv=True)
vae = vae.from_pretrained('cifar10-resnet18')

In this example stl10 weights have a different configuration for the encoder of the VAE. But, at the same time the internal method has a strict=False flag while loading so that users can load stl10 weights to the encoder configuration of cifar10 dataset.

1. Having this pattern allows us to test the correct loading of weights using the from_pretrained() function. @williamFalcon cases like the corrupt ImageNet weights for CPC will be caught automatically.

I have added all of this + tests for the AE and VAE classes I have updated for bolts.