Adding multiweight support for shufflenetv2 prototype model

torchvision/prototype/models/__init__.py

@@ -7,6 +7,7 @@
 from .mobilenetv2 import *
 from .mnasnet import *
 from .regnet import *
+from .shufflenetv2 import *
 from . import detection
 from . import quantization
 from . import segmentation

torchvision/prototype/models/shufflenetv2.py

@@ -0,0 +1,123 @@
+import warnings
+from functools import partial
+from typing import Any, Optional
+
+from torchvision.transforms.functional import InterpolationMode
+
+from ...models.shufflenetv2 import ShuffleNetV2
+from ..transforms.presets import ImageNetEval
+from ._api import Weights, WeightEntry
+from ._meta import _IMAGENET_CATEGORIES
+
+
+__all__ = [
+    "ShuffleNetV2",
+    "ShuffleNetV2_x0_5Weights",
+    "ShuffleNetV2_x1_0Weights",
+    "ShuffleNetV2_x1_5Weights",
+    "ShuffleNetV2_x2_0Weights",
+    "shufflenet_v2_x0_5",
+    "shufflenet_v2_x1_0",
+    "shufflenet_v2_x1_5",
+    "shufflenet_v2_x2_0",
+]
+
+
+def _shufflenetv2(
+    weights: Optional[Weights],
+    progress: bool,
+    *args: Any,
+    **kwargs: Any,
+) -> ShuffleNetV2:
+    if weights is not None:
+        kwargs["num_classes"] = len(weights.meta["categories"])
+
+    model = ShuffleNetV2(*args, **kwargs)
+
+    if weights is not None:
+        model.load_state_dict(weights.state_dict(progress=progress))
+
+    return model
+
+
+_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
+
+
+class ShuffleNetV2_x0_5Weights(Weights):
+    ImageNet1K_RefV1 = WeightEntry(
+        url="https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_common_meta,
+            "recipe": "",
+            "acc@1": 69.362,
+            "acc@5": 88.316,
+        },
+    )
+
+
+class ShuffleNetV2_x1_0Weights(Weights):
+    ImageNet1K_RefV1 = WeightEntry(
+        url="https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_common_meta,
+            "recipe": "",
+            "acc@1": 60.552,
+            "acc@5": 81.746,
+        },
+    )
+
+
+class ShuffleNetV2_x1_5Weights(Weights):
+    pass
+
+
+class ShuffleNetV2_x2_0Weights(Weights):
+    pass
+
+
+def shufflenet_v2_x0_5(
+    weights: Optional[ShuffleNetV2_x0_5Weights] = None, progress: bool = True, **kwargs: Any
+) -> ShuffleNetV2:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        weights = ShuffleNetV2_x0_5Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+    weights = ShuffleNetV2_x0_5Weights.verify(weights)
+
+    return _shufflenetv2(weights, progress, [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs)
+
+
+def shufflenet_v2_x1_0(
+    weights: Optional[ShuffleNetV2_x1_0Weights] = None, progress: bool = True, **kwargs: Any
+) -> ShuffleNetV2:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        weights = ShuffleNetV2_x1_0Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+    weights = ShuffleNetV2_x1_0Weights.verify(weights)
+
+    return _shufflenetv2(weights, progress, [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs)
+
+
+def shufflenet_v2_x1_5(
+    weights: Optional[ShuffleNetV2_x1_5Weights] = None, progress: bool = True, **kwargs: Any
+) -> ShuffleNetV2:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        if kwargs.pop("pretrained"):
+            raise ValueError("No checkpoint is available for model type shufflenet_v2_x1_5")
+    weights = ShuffleNetV2_x1_5Weights.verify(weights)
+
+    return _shufflenetv2(weights, progress, [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs)
+
+
+def shufflenet_v2_x2_0(
+    weights: Optional[ShuffleNetV2_x2_0Weights] = None, progress: bool = True, **kwargs: Any
+) -> ShuffleNetV2:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        if kwargs.pop("pretrained"):
+            raise ValueError("No checkpoint is available for model type shufflenet_v2_x2_0")
+    weights = ShuffleNetV2_x2_0Weights.verify(weights)
+
+    return _shufflenetv2(weights, progress, [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs)