Table of Contents
Shortcuts

# Source code for torchvision.models.quantization.resnet

    from functools import partial
    from typing import Any, List, Optional, Type, Union

    import torch
    import torch.nn as nn
    from torch import Tensor
    from torchvision.models.resnet import (
        BasicBlock,
        Bottleneck,
        ResNet,
        ResNet18_Weights,
        ResNet50_Weights,
        ResNeXt101_32X8D_Weights,
        ResNeXt101_64X4D_Weights,
    )

    from ...transforms._presets import ImageClassification
    from .._api import register_model, Weights, WeightsEnum
    from .._meta import _IMAGENET_CATEGORIES
    from .._utils import _ovewrite_named_param, handle_legacy_interface
    from .utils import _fuse_modules, _replace_relu, quantize_model

    __all__ = [
        "QuantizableResNet",
        "ResNet18_QuantizedWeights",
        "ResNet50_QuantizedWeights",
        "ResNeXt101_32X8D_QuantizedWeights",
        "ResNeXt101_64X4D_QuantizedWeights",
        "resnet18",
        "resnet50",
        "resnext101_32x8d",
        "resnext101_64x4d",
    ]

    class QuantizableBasicBlock(BasicBlock):
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, **kwargs)
            self.add_relu = torch.nn.quantized.FloatFunctional()

        def forward(self, x: Tensor) -> Tensor:
            identity = x

            out = self.conv1(x)
            out = self.bn1(out)
            out = self.relu(out)

            out = self.conv2(out)
            out = self.bn2(out)

            if self.downsample is not None:
                identity = self.downsample(x)

            out = self.add_relu.add_relu(out, identity)

            return out

        def fuse_model(self, is_qat: Optional[bool] = None) -> None:
            _fuse_modules(self, [["conv1", "bn1", "relu"], ["conv2", "bn2"]], is_qat, inplace=True)
            if self.downsample:
                _fuse_modules(self.downsample, ["0", "1"], is_qat, inplace=True)

    class QuantizableBottleneck(Bottleneck):
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, **kwargs)
            self.skip_add_relu = nn.quantized.FloatFunctional()
            self.relu1 = nn.ReLU(inplace=False)
            self.relu2 = nn.ReLU(inplace=False)

        def forward(self, x: Tensor) -> Tensor:
            identity = x
            out = self.conv1(x)
            out = self.bn1(out)
            out = self.relu1(out)
            out = self.conv2(out)
            out = self.bn2(out)
            out = self.relu2(out)

            out = self.conv3(out)
            out = self.bn3(out)

            if self.downsample is not None:
                identity = self.downsample(x)
            out = self.skip_add_relu.add_relu(out, identity)

            return out

        def fuse_model(self, is_qat: Optional[bool] = None) -> None:
            _fuse_modules(
                self, [["conv1", "bn1", "relu1"], ["conv2", "bn2", "relu2"], ["conv3", "bn3"]], is_qat, inplace=True
            )
            if self.downsample:
                _fuse_modules(self.downsample, ["0", "1"], is_qat, inplace=True)

    class QuantizableResNet(ResNet):
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, **kwargs)

            self.quant = torch.ao.quantization.QuantStub()
            self.dequant = torch.ao.quantization.DeQuantStub()

        def forward(self, x: Tensor) -> Tensor:
            x = self.quant(x)
            # Ensure scriptability
            # super(QuantizableResNet,self).forward(x)
            # is not scriptable
            x = self._forward_impl(x)
            x = self.dequant(x)
            return x

        def fuse_model(self, is_qat: Optional[bool] = None) -> None:
            r"""Fuse conv/bn/relu modules in resnet models

            Fuse conv+bn+relu/ Conv+relu/conv+Bn modules to prepare for quantization.
            Model is modified in place.  Note that this operation does not change numerics
            and the model after modification is in floating point
            """
            _fuse_modules(self, ["conv1", "bn1", "relu"], is_qat, inplace=True)
            for m in self.modules():
                if type(m) is QuantizableBottleneck or type(m) is QuantizableBasicBlock:
                    m.fuse_model(is_qat)

    def _resnet(
        block: Type[Union[QuantizableBasicBlock, QuantizableBottleneck]],
        layers: List[int],
        weights: Optional[WeightsEnum],
        progress: bool,
        quantize: bool,
        **kwargs: Any,
    ) -> QuantizableResNet:
        if weights is not None:
            _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
            if "backend" in weights.meta:
                _ovewrite_named_param(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.get_state_dict(progress=progress, check_hash=True))

        return model

    _COMMON_META = {
        "min_size": (1, 1),
        "categories": _IMAGENET_CATEGORIES,
        "backend": "fbgemm",
        "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models",
        "_docs": """
            These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized
            weights listed below.
        """,
    }

    class ResNet18_QuantizedWeights(WeightsEnum):
        IMAGENET1K_FBGEMM_V1 = Weights(
            url="https://download.pytorch.org/models/quantized/resnet18_fbgemm_16fa66dd.pth",
            transforms=partial(ImageClassification, crop_size=224),
            meta={
                **_COMMON_META,
                "num_params": 11689512,
                "unquantized": ResNet18_Weights.IMAGENET1K_V1,
                "_metrics": {
                    "ImageNet-1K": {
                        "acc@1": 69.494,
                        "acc@5": 88.882,
                    }
                },
                "_ops": 1.814,
                "_file_size": 11.238,
            },
        )
        DEFAULT = IMAGENET1K_FBGEMM_V1

    class ResNet50_QuantizedWeights(WeightsEnum):
        IMAGENET1K_FBGEMM_V1 = Weights(
            url="https://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pth",
            transforms=partial(ImageClassification, crop_size=224),
            meta={
                **_COMMON_META,
                "num_params": 25557032,
                "unquantized": ResNet50_Weights.IMAGENET1K_V1,
                "_metrics": {
                    "ImageNet-1K": {
                        "acc@1": 75.920,
                        "acc@5": 92.814,
                    }
                },
                "_ops": 4.089,
                "_file_size": 24.759,
            },
        )
        IMAGENET1K_FBGEMM_V2 = Weights(
            url="https://download.pytorch.org/models/quantized/resnet50_fbgemm-23753f79.pth",
            transforms=partial(ImageClassification, crop_size=224, resize_size=232),
            meta={
                **_COMMON_META,
                "num_params": 25557032,
                "unquantized": ResNet50_Weights.IMAGENET1K_V2,
                "_metrics": {
                    "ImageNet-1K": {
                        "acc@1": 80.282,
                        "acc@5": 94.976,
                    }
                },
                "_ops": 4.089,
                "_file_size": 24.953,
            },
        )
        DEFAULT = IMAGENET1K_FBGEMM_V2

    class ResNeXt101_32X8D_QuantizedWeights(WeightsEnum):
        IMAGENET1K_FBGEMM_V1 = Weights(
            url="https://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pth",
            transforms=partial(ImageClassification, crop_size=224),
            meta={
                **_COMMON_META,
                "num_params": 88791336,
                "unquantized": ResNeXt101_32X8D_Weights.IMAGENET1K_V1,
                "_metrics": {
                    "ImageNet-1K": {
                        "acc@1": 78.986,
                        "acc@5": 94.480,
                    }
                },
                "_ops": 16.414,
                "_file_size": 86.034,
            },
        )
        IMAGENET1K_FBGEMM_V2 = Weights(
            url="https://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm-ee16d00c.pth",
            transforms=partial(ImageClassification, crop_size=224, resize_size=232),
            meta={
                **_COMMON_META,
                "num_params": 88791336,
                "unquantized": ResNeXt101_32X8D_Weights.IMAGENET1K_V2,
                "_metrics": {
                    "ImageNet-1K": {
                        "acc@1": 82.574,
                        "acc@5": 96.132,
                    }
                },
                "_ops": 16.414,
                "_file_size": 86.645,
            },
        )
        DEFAULT = IMAGENET1K_FBGEMM_V2

    class ResNeXt101_64X4D_QuantizedWeights(WeightsEnum):
        IMAGENET1K_FBGEMM_V1 = Weights(
            url="https://download.pytorch.org/models/quantized/resnext101_64x4d_fbgemm-605a1cb3.pth",
            transforms=partial(ImageClassification, crop_size=224, resize_size=232),
            meta={
                **_COMMON_META,
                "num_params": 83455272,
                "recipe": "https://github.com/pytorch/vision/pull/5935",
                "unquantized": ResNeXt101_64X4D_Weights.IMAGENET1K_V1,
                "_metrics": {
                    "ImageNet-1K": {
                        "acc@1": 82.898,
                        "acc@5": 96.326,
                    }
                },
                "_ops": 15.46,
                "_file_size": 81.556,
            },
        )
        DEFAULT = IMAGENET1K_FBGEMM_V1

    @register_model(name="quantized_resnet18")
    @handle_legacy_interface(
        weights=(
            "pretrained",
            lambda kwargs: ResNet18_QuantizedWeights.IMAGENET1K_FBGEMM_V1
            if kwargs.get("quantize", False)
            else ResNet18_Weights.IMAGENET1K_V1,
        )
    )
    def resnet18(
        *,
        weights: Optional[Union[ResNet18_QuantizedWeights, ResNet18_Weights]] = None,
        progress: bool = True,
        quantize: bool = False,
        **kwargs: Any,
    ) -> QuantizableResNet:
        """ResNet-18 model from
        `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`_

        .. note::
            Note that ``quantize = True`` returns a quantized model with 8 bit
            weights. Quantized models only support inference and run on CPUs.
            GPU inference is not yet supported.

        Args:
            weights (:class:`~torchvision.models.quantization.ResNet18_QuantizedWeights` or :class:`~torchvision.models.ResNet18_Weights`, optional): The
                pretrained weights for the model. See
                :class:`~torchvision.models.quantization.ResNet18_QuantizedWeights` below for
                more details, and possible values. By default, no pre-trained
                weights are used.
            progress (bool, optional): If True, displays a progress bar of the
                download to stderr. Default is True.
            quantize (bool, optional): If True, return a quantized version of the model. Default is False.
            **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet``
                base class. Please refer to the `source code
                <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/resnet.py>`_
                for more details about this class.

        .. autoclass:: torchvision.models.quantization.ResNet18_QuantizedWeights
            :members:

        .. autoclass:: torchvision.models.ResNet18_Weights
            :members:
            :noindex:
        """
        weights = (ResNet18_QuantizedWeights if quantize else ResNet18_Weights).verify(weights)

        return _resnet(QuantizableBasicBlock, [2, 2, 2, 2], weights, progress, quantize, **kwargs)

    @register_model(name="quantized_resnet50")
    @handle_legacy_interface(
        weights=(
            "pretrained",
            lambda kwargs: ResNet50_QuantizedWeights.IMAGENET1K_FBGEMM_V1
            if kwargs.get("quantize", False)
            else ResNet50_Weights.IMAGENET1K_V1,
        )
    )
    def resnet50(
        *,
        weights: Optional[Union[ResNet50_QuantizedWeights, ResNet50_Weights]] = None,
        progress: bool = True,
        quantize: bool = False,
        **kwargs: Any,
    ) -> QuantizableResNet:
        """ResNet-50 model from
        `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`_

        .. note::
            Note that ``quantize = True`` returns a quantized model with 8 bit
            weights. Quantized models only support inference and run on CPUs.
            GPU inference is not yet supported.

        Args:
            weights (:class:`~torchvision.models.quantization.ResNet50_QuantizedWeights` or :class:`~torchvision.models.ResNet50_Weights`, optional): The
                pretrained weights for the model. See
                :class:`~torchvision.models.quantization.ResNet50_QuantizedWeights` below for
                more details, and possible values. By default, no pre-trained
                weights are used.
            progress (bool, optional): If True, displays a progress bar of the
                download to stderr. Default is True.
            quantize (bool, optional): If True, return a quantized version of the model. Default is False.
            **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet``
                base class. Please refer to the `source code
                <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/resnet.py>`_
                for more details about this class.

        .. autoclass:: torchvision.models.quantization.ResNet50_QuantizedWeights
            :members:

        .. autoclass:: torchvision.models.ResNet50_Weights
            :members:
            :noindex:
        """
        weights = (ResNet50_QuantizedWeights if quantize else ResNet50_Weights).verify(weights)

        return _resnet(QuantizableBottleneck, [3, 4, 6, 3], weights, progress, quantize, **kwargs)

    @register_model(name="quantized_resnext101_32x8d")
    @handle_legacy_interface(
        weights=(
            "pretrained",
            lambda kwargs: ResNeXt101_32X8D_QuantizedWeights.IMAGENET1K_FBGEMM_V1
            if kwargs.get("quantize", False)
            else ResNeXt101_32X8D_Weights.IMAGENET1K_V1,
        )
    )
    def resnext101_32x8d(
        *,
        weights: Optional[Union[ResNeXt101_32X8D_QuantizedWeights, ResNeXt101_32X8D_Weights]] = None,
        progress: bool = True,
        quantize: bool = False,
        **kwargs: Any,
    ) -> QuantizableResNet:
        """ResNeXt-101 32x8d model from
        `Aggregated Residual Transformation for Deep Neural Networks <https://arxiv.org/abs/1611.05431>`_

        .. note::
            Note that ``quantize = True`` returns a quantized model with 8 bit
            weights. Quantized models only support inference and run on CPUs.
            GPU inference is not yet supported.

        Args:
            weights (:class:`~torchvision.models.quantization.ResNeXt101_32X8D_QuantizedWeights` or :class:`~torchvision.models.ResNeXt101_32X8D_Weights`, optional): The
                pretrained weights for the model. See
                :class:`~torchvision.models.quantization.ResNet101_32X8D_QuantizedWeights` below for
                more details, and possible values. By default, no pre-trained
                weights are used.
            progress (bool, optional): If True, displays a progress bar of the
                download to stderr. Default is True.
            quantize (bool, optional): If True, return a quantized version of the model. Default is False.
            **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet``
                base class. Please refer to the `source code
                <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/resnet.py>`_
                for more details about this class.

        .. autoclass:: torchvision.models.quantization.ResNeXt101_32X8D_QuantizedWeights
            :members:

        .. autoclass:: torchvision.models.ResNeXt101_32X8D_Weights
            :members:
            :noindex:
        """
        weights = (ResNeXt101_32X8D_QuantizedWeights if quantize else ResNeXt101_32X8D_Weights).verify(weights)

        _ovewrite_named_param(kwargs, "groups", 32)
        _ovewrite_named_param(kwargs, "width_per_group", 8)
        return _resnet(QuantizableBottleneck, [3, 4, 23, 3], weights, progress, quantize, **kwargs)

    @register_model(name="quantized_resnext101_64x4d")
    @handle_legacy_interface(
        weights=(
            "pretrained",
            lambda kwargs: ResNeXt101_64X4D_QuantizedWeights.IMAGENET1K_FBGEMM_V1
            if kwargs.get("quantize", False)
            else ResNeXt101_64X4D_Weights.IMAGENET1K_V1,
        )
    )
    def resnext101_64x4d(
        *,
        weights: Optional[Union[ResNeXt101_64X4D_QuantizedWeights, ResNeXt101_64X4D_Weights]] = None,
        progress: bool = True,
        quantize: bool = False,
        **kwargs: Any,
    ) -> QuantizableResNet:
        """ResNeXt-101 64x4d model from
        `Aggregated Residual Transformation for Deep Neural Networks <https://arxiv.org/abs/1611.05431>`_

        .. note::
            Note that ``quantize = True`` returns a quantized model with 8 bit
            weights. Quantized models only support inference and run on CPUs.
            GPU inference is not yet supported.

        Args:
            weights (:class:`~torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights` or :class:`~torchvision.models.ResNeXt101_64X4D_Weights`, optional): The
                pretrained weights for the model. See
                :class:`~torchvision.models.quantization.ResNet101_64X4D_QuantizedWeights` below for
                more details, and possible values. By default, no pre-trained
                weights are used.
            progress (bool, optional): If True, displays a progress bar of the
                download to stderr. Default is True.
            quantize (bool, optional): If True, return a quantized version of the model. Default is False.
            **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet``
                base class. Please refer to the `source code
                <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/resnet.py>`_
                for more details about this class.

        .. autoclass:: torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights
            :members:

        .. autoclass:: torchvision.models.ResNeXt101_64X4D_Weights
            :members:
            :noindex:
        """
        weights = (ResNeXt101_64X4D_QuantizedWeights if quantize else ResNeXt101_64X4D_Weights).verify(weights)

        _ovewrite_named_param(kwargs, "groups", 64)
        _ovewrite_named_param(kwargs, "width_per_group", 4)
        return _resnet(QuantizableBottleneck, [3, 4, 23, 3], weights, progress, quantize, **kwargs)

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.