Port Multi-weight support from prototype to main (#5618)

* Moving basefiles outside of prototype and porting Alexnet, ConvNext, Densenet and EfficientNet.

* Porting googlenet

* Porting inception

* Porting mnasnet

* Porting mobilenetv2

* Porting mobilenetv3

* Porting regnet

* Porting resnet

* Porting shufflenetv2

* Porting squeezenet

* Porting vgg

* Porting vit

* Fix docstrings

* Fixing imports

* Adding missing import

* Fix mobilenet imports

* Fix tests

* Fix prototype tests

* Exclude get_weight from models on test

* Fix init files

* Porting googlenet

* Porting inception

* porting mobilenetv2

* porting mobilenetv3

* porting resnet

* porting shufflenetv2

* Fix test and linter

* Fixing docs.

* Porting Detection models (#5617)

* fix inits

* fix docs

* Port faster_rcnn

* Port fcos

* Port keypoint_rcnn

* Port mask_rcnn

* Port retinanet

* Port ssd

* Port ssdlite

* Fix linter

* Fixing tests

* Fi...

torchvision/models/quantization/__init__.py

-from .mobilenet import *
-from .resnet import *
 from .googlenet import *
 from .inception import *
+from .mobilenet import *
+from .resnet import *
 from .shufflenetv2 import *

torchvision/models/quantization/mobilenet.py

-from .mobilenetv2 import QuantizableMobileNetV2, mobilenet_v2, __all__ as mv2_all
-from .mobilenetv3 import QuantizableMobileNetV3, mobilenet_v3_large, __all__ as mv3_all
+from .mobilenetv2 import *  # noqa: F401, F403
+from .mobilenetv3 import *  # noqa: F401, F403
+from .mobilenetv2 import __all__ as mv2_all
+from .mobilenetv3 import __all__ as mv3_all
 
 __all__ = mv2_all + mv3_all

torchvision/models/segmentation/__init__.py

-from .fcn import *
 from .deeplabv3 import *
+from .fcn import *
 from .lraspp import *

torchvision/models/segmentation/_utils.py

@@ -4,7 +4,6 @@ from typing import Optional, Dict
 from torch import nn, Tensor
 from torch.nn import functional as F
 
-from ..._internally_replaced_utils import load_state_dict_from_url
 from ...utils import _log_api_usage_once
 
 
@@ -36,10 +35,3 @@ class _SimpleSegmentationModel(nn.Module):
             result["aux"] = x
 
         return result
-
-
-def _load_weights(arch: str, model: nn.Module, model_url: Optional[str], progress: bool) -> None:
-    if model_url is None:
-        raise ValueError(f"No checkpoint is available for {arch}")
-    state_dict = load_state_dict_from_url(model_url, progress=progress)
-    model.load_state_dict(state_dict)