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Adding multiweight support to Quantized MobileNetV2 and MobileNetV3 (#4859)
* Adding multiweight suport on Quant MobileNetV2 and MobileNetV3. * Fixing enum name. * Fixing lint.
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6 files changed

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‎torchvision/prototype/models/__init__.py‎

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from .googlenet import *
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from .inception import *
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from .mnasnet import *
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from .mobilenetv2 import *
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from .mobilenetv3 import *
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from .mobilenet import *
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from .regnet import *
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from .resnet import *
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from .shufflenetv2 import *

‎torchvision/prototype/models/mobilenet.py‎

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from .mobilenetv2 import * # noqa: F401, F403
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from .mobilenetv3 import * # noqa: F401, F403
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from .mobilenetv2 import __all__ as mv2_all
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from .mobilenetv3 import __all__ as mv3_all
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__all__ = mv2_all + mv3_all

‎torchvision/prototype/models/quantization/__init__.py‎

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

‎torchvision/prototype/models/quantization/mobilenet.py‎

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from .mobilenetv2 import * # noqa: F401, F403
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from .mobilenetv3 import * # noqa: F401, F403
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from .mobilenetv2 import __all__ as mv2_all
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from .mobilenetv3 import __all__ as mv3_all
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__all__ = mv2_all + mv3_all

‎torchvision/prototype/models/quantization/mobilenetv2.py‎

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import warnings
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from functools import partial
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from typing import Any, Optional, Union
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from torchvision.transforms.functional import InterpolationMode
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from ....models.quantization.mobilenetv2 import (
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QuantizableInvertedResidual,
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QuantizableMobileNetV2,
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_replace_relu,
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quantize_model,
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)
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from ...transforms.presets import ImageNetEval
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from .._api import Weights, WeightEntry
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from .._meta import _IMAGENET_CATEGORIES
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from ..mobilenetv2 import MobileNetV2Weights
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__all__ = [
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"QuantizableMobileNetV2",
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"QuantizedMobileNetV2Weights",
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"mobilenet_v2",
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]
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class QuantizedMobileNetV2Weights(Weights):
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ImageNet1K_QNNPACK_RefV1 = WeightEntry(
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url="https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth",
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transforms=partial(ImageNetEval, crop_size=224),
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meta={
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"size": (224, 224),
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"categories": _IMAGENET_CATEGORIES,
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"interpolation": InterpolationMode.BILINEAR,
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"backend": "qnnpack",
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"quantization": "qat",
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"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv2",
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"unquantized": MobileNetV2Weights.ImageNet1K_RefV1,
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"acc@1": 71.658,
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"acc@5": 90.150,
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},
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)
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def mobilenet_v2(
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weights: Optional[Union[QuantizedMobileNetV2Weights, MobileNetV2Weights]] = None,
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progress: bool = True,
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quantize: bool = False,
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**kwargs: Any,
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) -> QuantizableMobileNetV2:
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if "pretrained" in kwargs:
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warnings.warn("The argument pretrained is deprecated, please use weights instead.")
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if kwargs.pop("pretrained"):
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weights = (
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QuantizedMobileNetV2Weights.ImageNet1K_QNNPACK_RefV1
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if quantize
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else MobileNetV2Weights.ImageNet1K_RefV1
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)
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else:
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weights = None
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if quantize:
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weights = QuantizedMobileNetV2Weights.verify(weights)
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else:
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weights = MobileNetV2Weights.verify(weights)
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if weights is not None:
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kwargs["num_classes"] = len(weights.meta["categories"])
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if "backend" in weights.meta:
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kwargs["backend"] = weights.meta["backend"]
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backend = kwargs.pop("backend", "qnnpack")
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model = QuantizableMobileNetV2(block=QuantizableInvertedResidual, **kwargs)
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_replace_relu(model)
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if quantize:
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quantize_model(model, backend)
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if weights is not None:
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model.load_state_dict(weights.state_dict(progress=progress))
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return model

‎torchvision/prototype/models/quantization/mobilenetv3.py‎

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import warnings
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from functools import partial
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from typing import Any, List, Optional, Union
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import torch
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from torchvision.transforms.functional import InterpolationMode
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from ....models.quantization.mobilenetv3 import (
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InvertedResidualConfig,
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QuantizableInvertedResidual,
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QuantizableMobileNetV3,
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_replace_relu,
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)
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from ...transforms.presets import ImageNetEval
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from .._api import Weights, WeightEntry
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from .._meta import _IMAGENET_CATEGORIES
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from ..mobilenetv3 import MobileNetV3LargeWeights, _mobilenet_v3_conf
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__all__ = [
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"QuantizableMobileNetV3",
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"QuantizedMobileNetV3LargeWeights",
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"mobilenet_v3_large",
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]
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def _mobilenet_v3_model(
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inverted_residual_setting: List[InvertedResidualConfig],
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last_channel: int,
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weights: Optional[Weights],
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progress: bool,
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quantize: bool,
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**kwargs: Any,
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) -> QuantizableMobileNetV3:
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if weights is not None:
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kwargs["num_classes"] = len(weights.meta["categories"])
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if "backend" in weights.meta:
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kwargs["backend"] = weights.meta["backend"]
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backend = kwargs.pop("backend", "qnnpack")
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model = QuantizableMobileNetV3(inverted_residual_setting, last_channel, block=QuantizableInvertedResidual, **kwargs)
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_replace_relu(model)
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if quantize:
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model.fuse_model()
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model.qconfig = torch.quantization.get_default_qat_qconfig(backend)
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torch.quantization.prepare_qat(model, inplace=True)
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if weights is not None:
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model.load_state_dict(weights.state_dict(progress=progress))
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if quantize:
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torch.quantization.convert(model, inplace=True)
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model.eval()
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return model
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class QuantizedMobileNetV3LargeWeights(Weights):
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ImageNet1K_QNNPACK_RefV1 = WeightEntry(
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url="https://download.pytorch.org/models/quantized/mobilenet_v3_large_qnnpack-5bcacf28.pth",
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transforms=partial(ImageNetEval, crop_size=224),
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meta={
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"size": (224, 224),
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"categories": _IMAGENET_CATEGORIES,
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"interpolation": InterpolationMode.BILINEAR,
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"backend": "qnnpack",
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"quantization": "qat",
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"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv3",
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"unquantized": MobileNetV3LargeWeights.ImageNet1K_RefV1,
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"acc@1": 73.004,
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"acc@5": 90.858,
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},
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)
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def mobilenet_v3_large(
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weights: Optional[Union[QuantizedMobileNetV3LargeWeights, MobileNetV3LargeWeights]] = None,
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progress: bool = True,
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quantize: bool = False,
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**kwargs: Any,
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) -> QuantizableMobileNetV3:
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if "pretrained" in kwargs:
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warnings.warn("The argument pretrained is deprecated, please use weights instead.")
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if kwargs.pop("pretrained"):
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weights = (
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QuantizedMobileNetV3LargeWeights.ImageNet1K_QNNPACK_RefV1
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if quantize
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else MobileNetV3LargeWeights.ImageNet1K_RefV1
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)
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else:
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weights = None
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if quantize:
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weights = QuantizedMobileNetV3LargeWeights.verify(weights)
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else:
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weights = MobileNetV3LargeWeights.verify(weights)
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inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_large", **kwargs)
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return _mobilenet_v3_model(inverted_residual_setting, last_channel, weights, progress, quantize, **kwargs)

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