Multi-pretrained weight support - Quantized ResNet50 (#4627)

* Fixing minor issue on typing.

* Sample implementation for quantized resnet50.

torchvision/models/quantization/resnet.py

@@ -110,7 +110,7 @@ class QuantizableResNet(ResNet):
 
 def _resnet(
     arch: str,
-    block: Type[Union[BasicBlock, Bottleneck]],
+    block: Type[Union[QuantizableBasicBlock, QuantizableBottleneck]],
     layers: List[int],
     pretrained: bool,
     progress: bool,

torchvision/prototype/models/__init__.py

 from .resnet import *
 from . import detection
+from . import quantization

torchvision/prototype/models/quantization/__init__.py

+from .resnet import *

torchvision/prototype/models/quantization/resnet.py

+import warnings
+from functools import partial
+from typing import Any, List, Optional, Type, Union
+
+from ....models.quantization.resnet import (
+    QuantizableBasicBlock,
+    QuantizableBottleneck,
+    QuantizableResNet,
+    _replace_relu,
+    quantize_model,
+)
+from ...transforms.presets import ImageNetEval
+from .._api import Weights, WeightEntry
+from .._meta import _IMAGENET_CATEGORIES
+from ..resnet import ResNet50Weights
+
+
+__all__ = ["QuantizableResNet", "QuantizedResNet50Weights", "resnet50"]
+
+
+def _resnet(
+    block: Type[Union[QuantizableBasicBlock, QuantizableBottleneck]],
+    layers: List[int],
+    weights: Optional[Weights],
+    progress: bool,
+    quantize: bool,
+    **kwargs: Any,
+) -> QuantizableResNet:
+    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 = QuantizableResNet(block, layers, **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,
+    "backend": "fbgemm",
+}
+
+
+class QuantizedResNet50Weights(Weights):
+    ImageNet1K_FBGEMM_RefV1 = WeightEntry(
+        url="https://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_common_meta,
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#quantized",
+            "acc@1": 75.920,
+            "acc@5": 92.814,
+        },
+    )
+
+
+def resnet50(
+    weights: Optional[Union[QuantizedResNet50Weights, ResNet50Weights]] = None,
+    progress: bool = True,
+    quantize: bool = False,
+    **kwargs: Any,
+) -> QuantizableResNet:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        if kwargs.pop("pretrained"):
+            weights = QuantizedResNet50Weights.ImageNet1K_FBGEMM_RefV1 if quantize else ResNet50Weights.ImageNet1K_RefV1
+        else:
+            weights = None
+
+    if quantize:
+        weights = QuantizedResNet50Weights.verify(weights)
+    else:
+        weights = ResNet50Weights.verify(weights)
+
+    return _resnet(QuantizableBottleneck, [3, 4, 6, 3], weights, progress, quantize, **kwargs)