Fix bug - layer names and activation from previous refactor
# What does this PR do?

Removes two issues introduced by the PR, removing `nn.Sequential` subclasses: https://github.com/huggingface/transformers/commit/bdc01711d67161ef5c2097b6d2d885645e0a0f08

1. `nn.Sequential` block removed within an `__init__` causing differences in layer naming, resulting in weights not being loaded
2. Fixes logic issue where a final linear weight was missing in the maskformer model

All the models affected by the previous PR (maskformer, resnet, regnet, van) have been loaded in with their default checkpoint weights to double check all layers have their weights initialised, and all weights from the checkpoint are used. 

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. 
_The documentation is not available anymore as the PR was closed or merged._<|||||>Hey @amyeroberts! Could you show a snippet of where the current implementation is faulty (ideally for both resnet and maskformer)? Running a model load with either current `main` or this PR seems to result in the same layers being loaded/not loaded.<|||||>@LysandreJik - Sure. Here's how I found the issues (and checked it was working). 

The script I was running:
```
from transformers import VanForImageClassification, ResNetForImageClassification, RegNetForImageClassification, MaskFormerForInstanceSegmentation

print("\nLoading ResNet")
model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")

print("\nLoading VAN")
model = VanForImageClassification.from_pretrained("Visual-Attention-Network/van-base")

print("\nLoading RegNet")
model = RegNetForImageClassification.from_pretrained("facebook/regnet-y-040")

print("\nLoading MaskFormer")
model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-ade")
```

Running on `main` I get the following output:
```
(tenv) aroberts:transformers $ git checkout main
Already on 'main'
Your branch is ahead of 'origin/main' by 75 commits.
  (use "git push" to publish your local commits)
(tenv) aroberts:transformers $ python ../test_model_weight_loading.py

Loading ResNet
Some weights of the model checkpoint at microsoft/resnet-50 were not used when initializing ResNetForImageClassification: ['resnet.encoder.stages.1.layers.3.layer.0.normalization.weight', 'resnet.encoder.stages.0.layers.1.layer.2.convolution.weight', 'resnet.encoder.stages.1.layers.0.layer.1.normalization.running_var', 'resnet.encoder.stages.3.layers.0.shortcut.normalization.bias', 'resnet.encoder.stages.1.layers.0.layer.1.normalization.bias', 'resnet.encoder.stages.1.layers.0.layer.0.normalization.bias', 'resnet.encoder.stages.0.layers.1.layer.0.convolution.weight', 'resnet.encoder.stages.1.layers.2.layer.2.normalization.running_var', 'resnet.encoder.stages.0.layers.1.layer.2.normalization.running_mean', 'resnet.encoder.stages.1.layers.3.layer.2.normalization.running_mean', 'resnet.encoder.stages.2.layers.1.layer.0.normalization.running_mean', 'resnet.encoder.stages.2.layers.4.layer.0.convolution.weight', 'resnet.encoder.stages.2.layers.2.layer.2.normalization.running_mean', 'resnet.encoder.stages.3.layers.0.layer.1.normalization.running_mean', 'resnet.encoder.stages.2.layers.1.layer.0.convolution.weight', 'resnet.encoder.stages.2.layers.0.shortcut.normalization.num_batches_tracked', 'resnet.encoder.stages.1.layers.3.layer.0.normalization.bias', 'resnet.encoder.stages.3.layers.2.layer.0.normalization.bias', 'resnet.encoder.stages.1.layers.2.layer.2.normalization.running_mean', 'resnet.encoder.stages.2.layers.3.layer.1.normalization.num_batches_tracked', 'resnet.encoder.stages.0.layers.0.layer.0.normalization.bias', 'resnet.encoder.stages.0.layers.2.layer.2.normalization.running_mean', 'resnet.encoder.stages.1.layers.3.layer.1.normalization.num_batches_tracked', 'resnet.encoder.stages.2.layers.0.layer.0.normalization.bias', 'resnet.encoder.stages.3.layers.2.layer.0.normalization.weight', 'resnet.encoder.stages.0.layers.2.layer.0.normalization.running_mean', 'resnet.encoder.stages.1.layers.1.layer.2.normalization.running_var', 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'resnet.encoder.stages.3.layers.1.layer.2.normalization.bias', 'resnet.encoder.stages.3.layers.1.layer.2.convolution.weight', 'resnet.encoder.stages.2.layers.5.layer.1.normalization.running_mean', 'resnet.encoder.stages.2.layers.1.layer.1.normalization.bias', 'resnet.encoder.stages.1.layers.3.layer.2.normalization.weight', 'resnet.encoder.stages.2.layers.2.layer.1.normalization.num_batches_tracked', 'resnet.encoder.stages.2.layers.1.layer.1.normalization.num_batches_tracked', 'resnet.encoder.stages.1.layers.2.layer.1.normalization.running_var', 'resnet.encoder.stages.1.layers.2.layer.0.normalization.weight', 'resnet.encoder.stages.3.layers.2.layer.2.normalization.running_var', 'resnet.encoder.stages.1.layers.2.layer.2.convolution.weight', 'resnet.encoder.stages.3.layers.2.layer.0.normalization.running_mean', 'resnet.encoder.stages.3.layers.0.layer.1.normalization.running_var', 'resnet.encoder.stages.1.layers.0.layer.2.normalization.weight', 'resnet.encoder.stages.0.layers.0.layer.0.convolution.weight', 'resnet.encoder.stages.3.layers.2.layer.1.normalization.running_mean', 'resnet.encoder.stages.1.layers.1.layer.0.normalization.bias', 'resnet.encoder.stages.3.layers.0.layer.1.convolution.weight', 'resnet.encoder.stages.0.layers.0.layer.0.normalization.num_batches_tracked']
- This IS expected if you are initializing ResNetForImageClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing ResNetForImageClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of ResNetForImageClassification were not initialized from the model checkpoint at microsoft/resnet-50 and are newly initialized: ['resnet.encoder.stages.3.1.layer.1.normalization.bias', 'resnet.encoder.stages.3.1.layer.0.normalization.running_var', 'resnet.encoder.stages.3.0.layer.2.normalization.running_var', 'resnet.encoder.stages.1.0.shortcut.normalization.running_mean', 'resnet.encoder.stages.1.2.layer.2.normalization.num_batches_tracked', 'resnet.encoder.stages.1.3.layer.0.normalization.running_mean', 'resnet.encoder.stages.3.1.layer.1.normalization.weight', 'resnet.encoder.stages.2.4.layer.2.normalization.running_mean', 'resnet.encoder.stages.2.1.layer.1.normalization.running_mean', 'resnet.encoder.stages.0.2.layer.1.convolution.weight', 'resnet.encoder.stages.3.1.layer.2.normalization.bias', 'resnet.encoder.stages.2.0.layer.0.convolution.weight', 'resnet.encoder.stages.0.0.shortcut.normalization.weight', 'resnet.encoder.stages.1.0.shortcut.convolution.weight', 'resnet.encoder.stages.2.3.layer.0.normalization.weight', 'resnet.encoder.stages.1.1.layer.2.convolution.weight', 'resnet.encoder.stages.3.1.layer.2.normalization.num_batches_tracked', 'resnet.encoder.stages.1.3.layer.1.normalization.running_var', 'resnet.encoder.stages.0.1.layer.1.normalization.bias', 'resnet.encoder.stages.2.3.layer.1.convolution.weight', 'resnet.encoder.stages.2.4.layer.0.normalization.bias', 'resnet.encoder.stages.1.2.layer.2.normalization.bias', 'resnet.encoder.stages.3.0.layer.0.normalization.num_batches_tracked', 'resnet.encoder.stages.0.2.layer.2.normalization.bias', 'resnet.encoder.stages.0.1.layer.0.normalization.num_batches_tracked', 'resnet.encoder.stages.3.2.layer.2.normalization.running_var', 'resnet.encoder.stages.2.5.layer.0.normalization.bias', 'resnet.encoder.stages.2.0.layer.0.normalization.weight', 'resnet.encoder.stages.1.0.shortcut.normalization.weight', 'resnet.encoder.stages.3.2.layer.2.normalization.bias', 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'resnet.encoder.stages.0.0.layer.1.normalization.num_batches_tracked', 'resnet.encoder.stages.2.2.layer.0.normalization.bias', 'resnet.encoder.stages.2.0.layer.0.normalization.running_mean', 'resnet.encoder.stages.0.0.layer.0.normalization.bias', 'resnet.encoder.stages.3.0.layer.1.convolution.weight', 'resnet.encoder.stages.3.2.layer.1.normalization.weight', 'resnet.encoder.stages.2.1.layer.0.normalization.running_mean', 'resnet.encoder.stages.0.1.layer.0.normalization.bias', 'resnet.encoder.stages.3.1.layer.0.normalization.bias', 'resnet.encoder.stages.2.2.layer.1.normalization.bias', 'resnet.encoder.stages.1.1.layer.0.normalization.running_var', 'resnet.encoder.stages.0.1.layer.1.normalization.num_batches_tracked', 'resnet.encoder.stages.2.1.layer.1.normalization.bias', 'resnet.encoder.stages.3.0.layer.1.normalization.num_batches_tracked', 'resnet.encoder.stages.0.2.layer.2.normalization.running_mean', 'resnet.encoder.stages.2.1.layer.1.convolution.weight', 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'resnet.encoder.stages.3.0.shortcut.normalization.running_var', 'resnet.encoder.stages.1.3.layer.2.convolution.weight', 'resnet.encoder.stages.0.1.layer.1.normalization.running_mean', 'resnet.encoder.stages.0.0.layer.1.normalization.bias', 'resnet.encoder.stages.1.1.layer.1.normalization.weight', 'resnet.encoder.stages.1.0.layer.1.normalization.running_var', 'resnet.encoder.stages.2.1.layer.0.convolution.weight', 'resnet.encoder.stages.1.0.layer.1.normalization.running_mean', 'resnet.encoder.stages.3.2.layer.0.normalization.running_mean', 'resnet.encoder.stages.3.2.layer.0.normalization.weight', 'resnet.encoder.stages.2.2.layer.1.normalization.weight', 'resnet.encoder.stages.1.0.shortcut.normalization.bias', 'resnet.encoder.stages.0.0.layer.0.convolution.weight', 'resnet.encoder.stages.3.0.layer.1.normalization.weight', 'resnet.encoder.stages.1.0.layer.2.normalization.bias', 'resnet.encoder.stages.2.0.shortcut.normalization.running_mean', 'resnet.encoder.stages.2.1.layer.2.convolution.weight', 'resnet.encoder.stages.1.0.layer.0.normalization.num_batches_tracked', 'resnet.encoder.stages.2.5.layer.2.normalization.running_var', 'resnet.encoder.stages.3.1.layer.0.normalization.weight', 'resnet.encoder.stages.1.3.layer.0.normalization.running_var', 'resnet.encoder.stages.2.0.layer.1.convolution.weight', 'resnet.encoder.stages.1.1.layer.1.convolution.weight', 'resnet.encoder.stages.0.0.layer.2.normalization.weight', 'resnet.encoder.stages.0.0.layer.1.normalization.running_var', 'resnet.encoder.stages.1.1.layer.2.normalization.weight', 'resnet.encoder.stages.3.0.layer.2.normalization.running_mean', 'resnet.encoder.stages.1.1.layer.1.normalization.running_var', 'resnet.encoder.stages.1.2.layer.1.normalization.weight', 'resnet.encoder.stages.0.1.layer.2.normalization.num_batches_tracked', 'resnet.encoder.stages.2.2.layer.2.normalization.running_mean', 'resnet.encoder.stages.2.1.layer.2.normalization.running_mean', 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'resnet.encoder.stages.2.3.layer.2.normalization.running_var', 'resnet.encoder.stages.1.3.layer.0.normalization.bias', 'resnet.encoder.stages.2.5.layer.2.normalization.num_batches_tracked', 'resnet.encoder.stages.2.1.layer.0.normalization.weight', 'resnet.encoder.stages.2.4.layer.0.convolution.weight', 'resnet.encoder.stages.1.0.layer.1.normalization.bias', 'resnet.encoder.stages.1.0.layer.1.convolution.weight', 'resnet.encoder.stages.0.1.layer.0.normalization.weight', 'resnet.encoder.stages.2.2.layer.2.convolution.weight', 'resnet.encoder.stages.3.2.layer.0.normalization.num_batches_tracked', 'resnet.encoder.stages.1.3.layer.2.normalization.weight', 'resnet.encoder.stages.0.0.shortcut.normalization.running_mean', 'resnet.encoder.stages.1.1.layer.2.normalization.num_batches_tracked', 'resnet.encoder.stages.1.2.layer.2.normalization.running_var', 'resnet.encoder.stages.2.4.layer.2.convolution.weight', 'resnet.encoder.stages.2.5.layer.2.normalization.bias', 'resnet.encoder.stages.2.3.layer.0.normalization.num_batches_tracked', 'resnet.encoder.stages.2.3.layer.2.normalization.running_mean', 'resnet.encoder.stages.2.5.layer.1.normalization.running_var', 'resnet.encoder.stages.0.0.layer.2.normalization.num_batches_tracked', 'resnet.encoder.stages.2.3.layer.0.convolution.weight', 'resnet.encoder.stages.3.0.shortcut.normalization.weight', 'resnet.encoder.stages.0.0.layer.1.convolution.weight', 'resnet.encoder.stages.0.1.layer.2.convolution.weight', 'resnet.encoder.stages.1.1.layer.0.convolution.weight', 'resnet.encoder.stages.3.0.layer.2.convolution.weight', 'resnet.encoder.stages.1.3.layer.0.convolution.weight', 'resnet.encoder.stages.1.3.layer.1.normalization.bias', 'resnet.encoder.stages.0.1.layer.2.normalization.bias', 'resnet.encoder.stages.2.2.layer.2.normalization.bias', 'resnet.encoder.stages.1.3.layer.2.normalization.running_mean', 'resnet.encoder.stages.3.1.layer.1.normalization.running_mean', 'resnet.encoder.stages.2.1.layer.1.normalization.num_batches_tracked', 'resnet.encoder.stages.1.2.layer.2.normalization.weight', 'resnet.encoder.stages.0.0.layer.2.convolution.weight', 'resnet.encoder.stages.2.3.layer.1.normalization.bias', 'resnet.encoder.stages.2.2.layer.0.convolution.weight', 'resnet.encoder.stages.0.2.layer.0.normalization.bias', 'resnet.encoder.stages.1.1.layer.1.normalization.bias', 'resnet.encoder.stages.2.2.layer.2.normalization.running_var', 'resnet.encoder.stages.2.0.shortcut.convolution.weight', 'resnet.encoder.stages.0.1.layer.2.normalization.running_var', 'resnet.encoder.stages.3.0.layer.1.normalization.bias', 'resnet.encoder.stages.0.2.layer.2.normalization.running_var', 'resnet.encoder.stages.0.2.layer.2.normalization.num_batches_tracked', 'resnet.encoder.stages.2.0.shortcut.normalization.bias', 'resnet.encoder.stages.1.2.layer.1.normalization.running_mean', 'resnet.encoder.stages.2.2.layer.2.normalization.weight', 'resnet.encoder.stages.1.0.shortcut.normalization.running_var', 'resnet.encoder.stages.2.0.layer.0.normalization.bias', 'resnet.encoder.stages.0.0.shortcut.normalization.num_batches_tracked', 'resnet.encoder.stages.0.1.layer.0.normalization.running_mean', 'resnet.encoder.stages.1.0.layer.1.normalization.weight', 'resnet.encoder.stages.0.2.layer.0.normalization.running_mean', 'resnet.encoder.stages.1.3.layer.2.normalization.running_var', 'resnet.encoder.stages.1.2.layer.0.convolution.weight', 'resnet.encoder.stages.2.4.layer.0.normalization.running_mean', 'resnet.encoder.stages.3.1.layer.1.convolution.weight', 'resnet.encoder.stages.0.2.layer.0.normalization.num_batches_tracked', 'resnet.encoder.stages.1.3.layer.0.normalization.num_batches_tracked']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Loading VAN

Loading RegNet

Loading MaskFormer
/Users/aroberts/.virtualenvs/tenv/lib/python3.9/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  /Users/distiller/project/pytorch/aten/src/ATen/native/TensorShape.cpp:2228.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
Some weights of the model checkpoint at facebook/maskformer-swin-base-ade were not used when initializing MaskFormerForInstanceSegmentation: ['mask_embedder.2.0.weight', 'mask_embedder.2.0.bias']
- This IS expected if you are initializing MaskFormerForInstanceSegmentation from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing MaskFormerForInstanceSegmentation from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
```

Running on this branch - `fix-weight-naming`

```
(tenv) aroberts:transformers $ git checkout fix-weight-naming
Switched to branch 'fix-weight-naming'
(tenv) aroberts:transformers $ python ../test_model_weight_loading.py

Loading ResNet

Loading VAN

Loading RegNet

Loading MaskFormer
/Users/aroberts/.virtualenvs/tenv/lib/python3.9/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  /Users/distiller/project/pytorch/aten/src/ATen/native/TensorShape.cpp:2228.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
(tenv) aroberts:transformers $ 
```

Let me know if there's anything else you'd like me to run or check. 