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# Source code for torchvision.models.quantization.inception

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

    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    from torch import Tensor
    from torchvision.models import inception as inception_module
    from torchvision.models.inception import Inception_V3_Weights, InceptionOutputs

    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__ = [
        "QuantizableInception3",
        "Inception_V3_QuantizedWeights",
        "inception_v3",
    ]

    class QuantizableBasicConv2d(inception_module.BasicConv2d):
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, **kwargs)
            self.relu = nn.ReLU()

        def forward(self, x: Tensor) -> Tensor:
            x = self.conv(x)
            x = self.bn(x)
            x = self.relu(x)
            return x

        def fuse_model(self, is_qat: Optional[bool] = None) -> None:
            _fuse_modules(self, ["conv", "bn", "relu"], is_qat, inplace=True)

    class QuantizableInceptionA(inception_module.InceptionA):
        # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)  # type: ignore[misc]
            self.myop = nn.quantized.FloatFunctional()

        def forward(self, x: Tensor) -> Tensor:
            outputs = self._forward(x)
            return self.myop.cat(outputs, 1)

    class QuantizableInceptionB(inception_module.InceptionB):
        # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)  # type: ignore[misc]
            self.myop = nn.quantized.FloatFunctional()

        def forward(self, x: Tensor) -> Tensor:
            outputs = self._forward(x)
            return self.myop.cat(outputs, 1)

    class QuantizableInceptionC(inception_module.InceptionC):
        # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)  # type: ignore[misc]
            self.myop = nn.quantized.FloatFunctional()

        def forward(self, x: Tensor) -> Tensor:
            outputs = self._forward(x)
            return self.myop.cat(outputs, 1)

    class QuantizableInceptionD(inception_module.InceptionD):
        # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)  # type: ignore[misc]
            self.myop = nn.quantized.FloatFunctional()

        def forward(self, x: Tensor) -> Tensor:
            outputs = self._forward(x)
            return self.myop.cat(outputs, 1)

    class QuantizableInceptionE(inception_module.InceptionE):
        # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)  # type: ignore[misc]
            self.myop1 = nn.quantized.FloatFunctional()
            self.myop2 = nn.quantized.FloatFunctional()
            self.myop3 = nn.quantized.FloatFunctional()

        def _forward(self, x: Tensor) -> List[Tensor]:
            branch1x1 = self.branch1x1(x)

            branch3x3 = self.branch3x3_1(x)
            branch3x3 = [self.branch3x3_2a(branch3x3), self.branch3x3_2b(branch3x3)]
            branch3x3 = self.myop1.cat(branch3x3, 1)

            branch3x3dbl = self.branch3x3dbl_1(x)
            branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
            branch3x3dbl = [
                self.branch3x3dbl_3a(branch3x3dbl),
                self.branch3x3dbl_3b(branch3x3dbl),
            ]
            branch3x3dbl = self.myop2.cat(branch3x3dbl, 1)

            branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
            branch_pool = self.branch_pool(branch_pool)

            outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
            return outputs

        def forward(self, x: Tensor) -> Tensor:
            outputs = self._forward(x)
            return self.myop3.cat(outputs, 1)

    class QuantizableInceptionAux(inception_module.InceptionAux):
        # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)  # type: ignore[misc]

    class QuantizableInception3(inception_module.Inception3):
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(  # type: ignore[misc]
                *args,
                inception_blocks=[
                    QuantizableBasicConv2d,
                    QuantizableInceptionA,
                    QuantizableInceptionB,
                    QuantizableInceptionC,
                    QuantizableInceptionD,
                    QuantizableInceptionE,
                    QuantizableInceptionAux,
                ],
                **kwargs,
            )
            self.quant = torch.ao.quantization.QuantStub()
            self.dequant = torch.ao.quantization.DeQuantStub()

        def forward(self, x: Tensor) -> InceptionOutputs:
            x = self._transform_input(x)
            x = self.quant(x)
            x, aux = self._forward(x)
            x = self.dequant(x)
            aux_defined = self.training and self.aux_logits
            if torch.jit.is_scripting():
                if not aux_defined:
                    warnings.warn("Scripted QuantizableInception3 always returns QuantizableInception3 Tuple")
                return InceptionOutputs(x, aux)
            else:
                return self.eager_outputs(x, aux)

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

            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
            """

            for m in self.modules():
                if type(m) is QuantizableBasicConv2d:
                    m.fuse_model(is_qat)

    class Inception_V3_QuantizedWeights(WeightsEnum):
        IMAGENET1K_FBGEMM_V1 = Weights(
            url="https://download.pytorch.org/models/quantized/inception_v3_google_fbgemm-a2837893.pth",
            transforms=partial(ImageClassification, crop_size=299, resize_size=342),
            meta={
                "num_params": 27161264,
                "min_size": (75, 75),
                "categories": _IMAGENET_CATEGORIES,
                "backend": "fbgemm",
                "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models",
                "unquantized": Inception_V3_Weights.IMAGENET1K_V1,
                "_metrics": {
                    "ImageNet-1K": {
                        "acc@1": 77.176,
                        "acc@5": 93.354,
                    }
                },
                "_ops": 5.713,
                "_file_size": 23.146,
                "_docs": """
                    These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized
                    weights listed below.
                """,
            },
        )
        DEFAULT = IMAGENET1K_FBGEMM_V1

    @register_model(name="quantized_inception_v3")
    @handle_legacy_interface(
        weights=(
            "pretrained",
            lambda kwargs: Inception_V3_QuantizedWeights.IMAGENET1K_FBGEMM_V1
            if kwargs.get("quantize", False)
            else Inception_V3_Weights.IMAGENET1K_V1,
        )
    )
    def inception_v3(
        *,
        weights: Optional[Union[Inception_V3_QuantizedWeights, Inception_V3_Weights]] = None,
        progress: bool = True,
        quantize: bool = False,
        **kwargs: Any,
    ) -> QuantizableInception3:
        r"""Inception v3 model architecture from
        `Rethinking the Inception Architecture for Computer Vision <http://arxiv.org/abs/1512.00567>`__.

        .. note::
            **Important**: In contrast to the other models the inception_v3 expects tensors with a size of
            N x 3 x 299 x 299, so ensure your images are sized accordingly.

        .. 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.Inception_V3_QuantizedWeights` or :class:`~torchvision.models.Inception_V3_Weights`, optional): The pretrained
                weights for the model. See
                :class:`~torchvision.models.quantization.Inception_V3_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.QuantizableInception3``
                base class. Please refer to the `source code
                <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/inception.py>`_
                for more details about this class.

        .. autoclass:: torchvision.models.quantization.Inception_V3_QuantizedWeights
            :members:

        .. autoclass:: torchvision.models.Inception_V3_Weights
            :members:
            :noindex:
        """
        weights = (Inception_V3_QuantizedWeights if quantize else Inception_V3_Weights).verify(weights)

        original_aux_logits = kwargs.get("aux_logits", False)
        if weights is not None:
            if "transform_input" not in kwargs:
                _ovewrite_named_param(kwargs, "transform_input", True)
            _ovewrite_named_param(kwargs, "aux_logits", True)
            _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 = QuantizableInception3(**kwargs)
        _replace_relu(model)
        if quantize:
            quantize_model(model, backend)

        if weights is not None:
            if quantize and not original_aux_logits:
                model.aux_logits = False
                model.AuxLogits = None
            model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
            if not quantize and not original_aux_logits:
                model.aux_logits = False
                model.AuxLogits = None

        return model

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