Multi-weight support for DeepLabV3 prototype models (#4757)

* adding multiweight support for deeplabv3 prototype models

* adding default values for optional params

* fixing bug

* addressing PR comment

torchvision/prototype/models/segmentation/__init__.py

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

torchvision/prototype/models/segmentation/deeplabv3.py

+import warnings
+from functools import partial
+from typing import Any, Optional
+
+from ....models.segmentation.deeplabv3 import DeepLabV3, _deeplabv3_mobilenetv3, _deeplabv3_resnet
+from ...transforms.presets import VocEval
+from .._api import Weights, WeightEntry
+from .._meta import _VOC_CATEGORIES
+from ..mobilenetv3 import MobileNetV3LargeWeights, mobilenet_v3_large
+from ..resnet import resnet50, resnet101
+from ..resnet import ResNet50Weights, ResNet101Weights
+
+
+__all__ = [
+    "DeepLabV3",
+    "DeepLabV3ResNet50Weights",
+    "DeepLabV3ResNet101Weights",
+    "DeepLabV3MobileNetV3LargeWeights",
+    "deeplabv3_mobilenet_v3_large",
+    "deeplabv3_resnet50",
+    "deeplabv3_resnet101",
+]
+
+
+class DeepLabV3ResNet50Weights(Weights):
+    CocoWithVocLabels_RefV1 = WeightEntry(
+        url="https://download.pytorch.org/models/deeplabv3_resnet50_coco-cd0a2569.pth",
+        transforms=partial(VocEval, resize_size=520),
+        meta={
+            "categories": _VOC_CATEGORIES,
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_resnet50",
+            "mIoU": 66.4,
+            "acc": 92.4,
+        },
+    )
+
+
+class DeepLabV3ResNet101Weights(Weights):
+    CocoWithVocLabels_RefV1 = WeightEntry(
+        url="https://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pth",
+        transforms=partial(VocEval, resize_size=520),
+        meta={
+            "categories": _VOC_CATEGORIES,
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#fcn_resnet101",
+            "mIoU": 67.4,
+            "acc": 92.4,
+        },
+    )
+
+
+class DeepLabV3MobileNetV3LargeWeights(Weights):
+    CocoWithVocLabels_RefV1 = WeightEntry(
+        url="https://download.pytorch.org/models/deeplabv3_mobilenet_v3_large-fc3c493d.pth",
+        transforms=partial(VocEval, resize_size=520),
+        meta={
+            "categories": _VOC_CATEGORIES,
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_mobilenet_v3_large",
+            "mIoU": 60.3,
+            "acc": 91.2,
+        },
+    )
+
+
+def deeplabv3_resnet50(
+    weights: Optional[DeepLabV3ResNet50Weights] = None,
+    weights_backbone: Optional[ResNet50Weights] = None,
+    progress: bool = True,
+    num_classes: int = 21,
+    aux_loss: Optional[bool] = None,
+    **kwargs: Any,
+) -> DeepLabV3:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        weights = DeepLabV3ResNet50Weights.CocoWithVocLabels_RefV1 if kwargs.pop("pretrained") else None
+
+    weights = DeepLabV3ResNet50Weights.verify(weights)
+    if "pretrained_backbone" in kwargs:
+        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        weights_backbone = ResNet50Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
+    weights_backbone = ResNet50Weights.verify(weights_backbone)
+
+    if weights is not None:
+        weights_backbone = None
+        aux_loss = True
+        num_classes = len(weights.meta["categories"])
+
+    backbone = resnet50(weights=weights_backbone, replace_stride_with_dilation=[False, True, True])
+    model = _deeplabv3_resnet(backbone, num_classes, aux_loss)
+
+    if weights is not None:
+        model.load_state_dict(weights.state_dict(progress=progress))
+
+    return model
+
+
+def deeplabv3_resnet101(
+    weights: Optional[DeepLabV3ResNet101Weights] = None,
+    weights_backbone: Optional[ResNet101Weights] = None,
+    progress: bool = True,
+    num_classes: int = 21,
+    aux_loss: Optional[bool] = None,
+    **kwargs: Any,
+) -> DeepLabV3:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        weights = DeepLabV3ResNet101Weights.CocoWithVocLabels_RefV1 if kwargs.pop("pretrained") else None
+
+    weights = DeepLabV3ResNet101Weights.verify(weights)
+    if "pretrained_backbone" in kwargs:
+        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        weights_backbone = ResNet101Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
+    weights_backbone = ResNet101Weights.verify(weights_backbone)
+
+    if weights is not None:
+        weights_backbone = None
+        aux_loss = True
+        num_classes = len(weights.meta["categories"])
+
+    backbone = resnet101(weights=weights_backbone, replace_stride_with_dilation=[False, True, True])
+    model = _deeplabv3_resnet(backbone, num_classes, aux_loss)
+
+    if weights is not None:
+        model.load_state_dict(weights.state_dict(progress=progress))
+
+    return model
+
+
+def deeplabv3_mobilenet_v3_large(
+    weights: Optional[DeepLabV3MobileNetV3LargeWeights] = None,
+    weights_backbone: Optional[MobileNetV3LargeWeights] = None,
+    progress: bool = True,
+    num_classes: int = 21,
+    aux_loss: Optional[bool] = None,
+    **kwargs: Any,
+) -> DeepLabV3:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        weights = DeepLabV3MobileNetV3LargeWeights.CocoWithVocLabels_RefV1 if kwargs.pop("pretrained") else None
+
+    weights = DeepLabV3MobileNetV3LargeWeights.verify(weights)
+    if "pretrained_backbone" in kwargs:
+        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        weights_backbone = MobileNetV3LargeWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
+    weights_backbone = MobileNetV3LargeWeights.verify(weights_backbone)
+
+    if weights is not None:
+        weights_backbone = None
+        aux_loss = True
+        num_classes = len(weights.meta["categories"])
+
+    backbone = mobilenet_v3_large(weights=weights_backbone, dilated=True)
+    model = _deeplabv3_mobilenetv3(backbone, num_classes, aux_loss)
+
+    if weights is not None:
+        model.load_state_dict(weights.state_dict(progress=progress))
+
+    return model