Adding multiweight suport on Quant ShuffleNetV2 (#4856)

torchvision/prototype/models/quantization/__init__.py

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

torchvision/prototype/models/quantization/shufflenetv2.py

+import warnings
+from functools import partial
+from typing import Any, List, Optional, Union
+
+from torchvision.transforms.functional import InterpolationMode
+
+from ....models.quantization.shufflenetv2 import (
+    QuantizableShuffleNetV2,
+    _replace_relu,
+    quantize_model,
+)
+from ...transforms.presets import ImageNetEval
+from .._api import Weights, WeightEntry
+from .._meta import _IMAGENET_CATEGORIES
+from ..shufflenetv2 import ShuffleNetV2_x0_5Weights, ShuffleNetV2_x1_0Weights
+
+
+__all__ = [
+    "QuantizableShuffleNetV2",
+    "QuantizedShuffleNetV2_x0_5Weights",
+    "QuantizedShuffleNetV2_x1_0Weights",
+    "shufflenet_v2_x0_5",
+    "shufflenet_v2_x1_0",
+]
+
+
+def _shufflenetv2(
+    stages_repeats: List[int],
+    stages_out_channels: List[int],
+    weights: Optional[Weights],
+    progress: bool,
+    quantize: bool,
+    **kwargs: Any,
+) -> QuantizableShuffleNetV2:
+    if weights is not None:
+        kwargs["num_classes"] = len(weights.meta["categories"])
+        if "backend" in weights.meta:
+            kwargs["backend"] = weights.meta["backend"]
+    backend = kwargs.pop("backend", "fbgemm")
+
+    model = QuantizableShuffleNetV2(stages_repeats, stages_out_channels, **kwargs)
+    _replace_relu(model)
+    if quantize:
+        quantize_model(model, backend)
+
+    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,
+    "backend": "fbgemm",
+    "quantization": "ptq",
+    "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models",
+}
+
+
+class QuantizedShuffleNetV2_x0_5Weights(Weights):
+    ImageNet1K_FBGEMM_Community = WeightEntry(
+        url="https://download.pytorch.org/models/quantized/shufflenetv2_x0.5_fbgemm-00845098.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_common_meta,
+            "unquantized": ShuffleNetV2_x0_5Weights.ImageNet1K_Community,
+            "acc@1": 57.972,
+            "acc@5": 79.780,
+        },
+    )
+
+
+class QuantizedShuffleNetV2_x1_0Weights(Weights):
+    ImageNet1K_FBGEMM_Community = WeightEntry(
+        url="https://download.pytorch.org/models/quantized/shufflenetv2_x1_fbgemm-db332c57.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_common_meta,
+            "unquantized": ShuffleNetV2_x1_0Weights.ImageNet1K_Community,
+            "acc@1": 68.360,
+            "acc@5": 87.582,
+        },
+    )
+
+
+def shufflenet_v2_x0_5(
+    weights: Optional[Union[QuantizedShuffleNetV2_x0_5Weights, ShuffleNetV2_x0_5Weights]] = None,
+    progress: bool = True,
+    quantize: bool = False,
+    **kwargs: Any,
+) -> QuantizableShuffleNetV2:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        if kwargs.pop("pretrained"):
+            weights = (
+                QuantizedShuffleNetV2_x0_5Weights.ImageNet1K_FBGEMM_Community
+                if quantize
+                else ShuffleNetV2_x0_5Weights.ImageNet1K_Community
+            )
+        else:
+            weights = None
+
+    if quantize:
+        weights = QuantizedShuffleNetV2_x0_5Weights.verify(weights)
+    else:
+        weights = ShuffleNetV2_x0_5Weights.verify(weights)
+
+    return _shufflenetv2([4, 8, 4], [24, 48, 96, 192, 1024], weights, progress, quantize, **kwargs)
+
+
+def shufflenet_v2_x1_0(
+    weights: Optional[Union[QuantizedShuffleNetV2_x1_0Weights, ShuffleNetV2_x1_0Weights]] = None,
+    progress: bool = True,
+    quantize: bool = False,
+    **kwargs: Any,
+) -> QuantizableShuffleNetV2:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        if kwargs.pop("pretrained"):
+            weights = (
+                QuantizedShuffleNetV2_x1_0Weights.ImageNet1K_FBGEMM_Community
+                if quantize
+                else ShuffleNetV2_x1_0Weights.ImageNet1K_Community
+            )
+        else:
+            weights = None
+
+    if quantize:
+        weights = QuantizedShuffleNetV2_x1_0Weights.verify(weights)
+    else:
+        weights = ShuffleNetV2_x1_0Weights.verify(weights)
+
+    return _shufflenetv2([4, 8, 4], [24, 116, 232, 464, 1024], weights, progress, quantize, **kwargs)