Overhaul 2.1 Remove Dependencies / Add Full Timm Support

[isaaccorley]

This PR seeks to do a few things:

• remove the dependency on pretrainedmodels and efficientnet-pytorch as they are no longer maintained.
• remove the mock and torchvision dependency (torchvision isn't used anywhere anyway)
• Replace any lost model support by fully using timm (torchseg.encoders.TimmEncoder)
• add support for timm ViT encoders (torchseg.encoders.TimmViTEncoder`)
• Test all supported and unsupported timm encoders (they are defined here torchseg.encoders.supported). This includes ConvNext and Swin pretrained backbones
• Update tests to be more thorough
• Remove unecessary forward/backward calls in tests (this speeds up the tests significantly)

There's some other misc cleanup that is done:

• remove Activation class. We instead let the user choose the head activation when creating a model. Still defaults to nn.Identity()
• Allow for passing additional encoder_params to timm.create_model(**kwargs) in case users want to further customize the backbone

[JulienMaille]

Looking forward to test this update!
Have you checked if you can use timm convenext_nano encoder with this PR?

[isaaccorley]

Looking forward to test this update!

Have you checked if you can use timm convenext_nano encoder with this PR?

Yes this PR allows the use of convnext as the encoder!

[isaaccorley]

@notprime Can you give this a review and maybe give some thoughts?

[ogencoglu]

Looking forward to this update...

[notprime]

Everything seems fine to me, maybe we can specify act_layer and norm_layer directly in params instead of adding them through params.update via kwargs, just to make it clearer

[isaaccorley]

@JulienMaille @ogencoglu @notprime These features are now merged and you can install them using

pip install --pre torchseg or pip install 'torchseg==0.0.1a2'