Adding multiweight support for squeezenet prototype model (#4817)

* Change enum name for weights contributed by community.

* Adding multiweight support to squeezenet.

torchvision/prototype/models/__init__.py

@@ -8,6 +8,7 @@ from .mobilenetv3 import *
 from .regnet import *
 from .resnet import *
 from .shufflenetv2 import *
+from .squeezenet import *
 from .vgg import *
 from . import detection
 from . import quantization

torchvision/prototype/models/googlenet.py

@@ -17,7 +17,7 @@ _common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpo
 
 
 class GoogLeNetWeights(Weights):
-    ImageNet1K_TheCodezV1 = WeightEntry(
+    ImageNet1K_Community = WeightEntry(
         url="https://download.pytorch.org/models/googlenet-1378be20.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -32,7 +32,7 @@ class GoogLeNetWeights(Weights):
 def googlenet(weights: Optional[GoogLeNetWeights] = None, progress: bool = True, **kwargs: Any) -> GoogLeNet:
     if "pretrained" in kwargs:
         warnings.warn("The argument pretrained is deprecated, please use weights instead.")
-        weights = GoogLeNetWeights.ImageNet1K_TheCodezV1 if kwargs.pop("pretrained") else None
+        weights = GoogLeNetWeights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = GoogLeNetWeights.verify(weights)
 
     original_aux_logits = kwargs.get("aux_logits", False)

torchvision/prototype/models/mnasnet.py

@@ -27,7 +27,7 @@ _common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpo
 
 
 class MNASNet0_5Weights(Weights):
-    ImageNet1K_TrainerV1 = WeightEntry(
+    ImageNet1K_Community = WeightEntry(
         url="https://download.pytorch.org/models/mnasnet0.5_top1_67.823-3ffadce67e.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -45,7 +45,7 @@ class MNASNet0_75Weights(Weights):
 
 
 class MNASNet1_0Weights(Weights):
-    ImageNet1K_TrainerV1 = WeightEntry(
+    ImageNet1K_Community = WeightEntry(
         url="https://download.pytorch.org/models/mnasnet1.0_top1_73.512-f206786ef8.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -77,7 +77,7 @@ def _mnasnet(alpha: float, weights: Optional[Weights], progress: bool, **kwargs:
 def mnasnet0_5(weights: Optional[MNASNet0_5Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
     if "pretrained" in kwargs:
         warnings.warn("The argument pretrained is deprecated, please use weights instead.")
-        weights = MNASNet0_5Weights.ImageNet1K_TrainerV1 if kwargs.pop("pretrained") else None
+        weights = MNASNet0_5Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
 
     weights = MNASNet0_5Weights.verify(weights)
 
@@ -98,7 +98,7 @@ def mnasnet0_75(weights: Optional[MNASNet0_75Weights] = None, progress: bool = T
 def mnasnet1_0(weights: Optional[MNASNet1_0Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
     if "pretrained" in kwargs:
         warnings.warn("The argument pretrained is deprecated, please use weights instead.")
-        weights = MNASNet1_0Weights.ImageNet1K_TrainerV1 if kwargs.pop("pretrained") else None
+        weights = MNASNet1_0Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = MNASNet1_0Weights.verify(weights)
 
     return _mnasnet(1.0, weights, progress, **kwargs)

torchvision/prototype/models/squeezenet.py

+import warnings
+from functools import partial
+from typing import Any, Optional
+
+from torchvision.transforms.functional import InterpolationMode
+
+from ...models.squeezenet import SqueezeNet
+from ..transforms.presets import ImageNetEval
+from ._api import Weights, WeightEntry
+from ._meta import _IMAGENET_CATEGORIES
+
+
+__all__ = ["SqueezeNet", "SqueezeNet1_0Weights", "SqueezeNet1_1Weights", "squeezenet1_0", "squeezenet1_1"]
+
+
+_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
+
+
+class SqueezeNet1_0Weights(Weights):
+    ImageNet1K_Community = WeightEntry(
+        url="https://download.pytorch.org/models/squeezenet1_0-b66bff10.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_common_meta,
+            "recipe": "https://github.com/pytorch/vision/pull/49#issuecomment-277560717",
+            "acc@1": 58.092,
+            "acc@5": 80.420,
+        },
+    )
+
+
+class SqueezeNet1_1Weights(Weights):
+    ImageNet1K_Community = WeightEntry(
+        url="https://download.pytorch.org/models/squeezenet1_1-b8a52dc0.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_common_meta,
+            "recipe": "https://github.com/pytorch/vision/pull/49#issuecomment-277560717",
+            "acc@1": 58.178,
+            "acc@5": 80.624,
+        },
+    )
+
+
+def squeezenet1_0(weights: Optional[SqueezeNet1_0Weights] = None, progress: bool = True, **kwargs: Any) -> SqueezeNet:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        weights = SqueezeNet1_0Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
+    weights = SqueezeNet1_0Weights.verify(weights)
+    if weights is not None:
+        kwargs["num_classes"] = len(weights.meta["categories"])
+
+    model = SqueezeNet("1_0", **kwargs)
+
+    if weights is not None:
+        model.load_state_dict(weights.state_dict(progress=progress))
+
+    return model
+
+
+def squeezenet1_1(weights: Optional[SqueezeNet1_1Weights] = None, progress: bool = True, **kwargs: Any) -> SqueezeNet:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        weights = SqueezeNet1_1Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
+    weights = SqueezeNet1_1Weights.verify(weights)
+    if weights is not None:
+        kwargs["num_classes"] = len(weights.meta["categories"])
+
+    model = SqueezeNet("1_1", **kwargs)
+
+    if weights is not None:
+        model.load_state_dict(weights.state_dict(progress=progress))
+
+    return model