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# Source code for torchvision.models.quantization.googlenet

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

    import torch
    import torch.nn as nn
    from torch import Tensor
    from torch.nn import functional as F

    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 ..googlenet import BasicConv2d, GoogLeNet, GoogLeNet_Weights, GoogLeNetOutputs, Inception, InceptionAux
    from .utils import _fuse_modules, _replace_relu, quantize_model

    __all__ = [
        "QuantizableGoogLeNet",
        "GoogLeNet_QuantizedWeights",
        "googlenet",
    ]

    class QuantizableBasicConv2d(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 QuantizableInception(Inception):
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)  # type: ignore[misc]
            self.cat = nn.quantized.FloatFunctional()

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

    class QuantizableInceptionAux(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]
            self.relu = nn.ReLU()

        def forward(self, x: Tensor) -> Tensor:
            # aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
            x = F.adaptive_avg_pool2d(x, (4, 4))
            # aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
            x = self.conv(x)
            # N x 128 x 4 x 4
            x = torch.flatten(x, 1)
            # N x 2048
            x = self.relu(self.fc1(x))
            # N x 1024
            x = self.dropout(x)
            # N x 1024
            x = self.fc2(x)
            # N x 1000 (num_classes)

            return x

    class QuantizableGoogLeNet(GoogLeNet):
        # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(  # type: ignore[misc]
                *args, blocks=[QuantizableBasicConv2d, QuantizableInception, QuantizableInceptionAux], **kwargs
            )
            self.quant = torch.ao.quantization.QuantStub()
            self.dequant = torch.ao.quantization.DeQuantStub()

        def forward(self, x: Tensor) -> GoogLeNetOutputs:
            x = self._transform_input(x)
            x = self.quant(x)
            x, aux1, aux2 = 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 QuantizableGoogleNet always returns GoogleNetOutputs Tuple")
                return GoogLeNetOutputs(x, aux2, aux1)
            else:
                return self.eager_outputs(x, aux2, aux1)

        def fuse_model(self, is_qat: Optional[bool] = None) -> None:
            r"""Fuse conv/bn/relu modules in googlenet 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 GoogLeNet_QuantizedWeights(WeightsEnum):
        IMAGENET1K_FBGEMM_V1 = Weights(
            url="https://download.pytorch.org/models/quantized/googlenet_fbgemm-c81f6644.pth",
            transforms=partial(ImageClassification, crop_size=224),
            meta={
                "num_params": 6624904,
                "min_size": (15, 15),
                "categories": _IMAGENET_CATEGORIES,
                "backend": "fbgemm",
                "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models",
                "unquantized": GoogLeNet_Weights.IMAGENET1K_V1,
                "_metrics": {
                    "ImageNet-1K": {
                        "acc@1": 69.826,
                        "acc@5": 89.404,
                    }
                },
                "_ops": 1.498,
                "_file_size": 12.618,
                "_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_googlenet")
    @handle_legacy_interface(
        weights=(
            "pretrained",
            lambda kwargs: GoogLeNet_QuantizedWeights.IMAGENET1K_FBGEMM_V1
            if kwargs.get("quantize", False)
            else GoogLeNet_Weights.IMAGENET1K_V1,
        )
    )
    def googlenet(
        *,
        weights: Optional[Union[GoogLeNet_QuantizedWeights, GoogLeNet_Weights]] = None,
        progress: bool = True,
        quantize: bool = False,
        **kwargs: Any,
    ) -> QuantizableGoogLeNet:
        """GoogLeNet (Inception v1) model architecture from `Going Deeper with Convolutions <http://arxiv.org/abs/1409.4842>`__.

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

        .. autoclass:: torchvision.models.quantization.GoogLeNet_QuantizedWeights
            :members:

        .. autoclass:: torchvision.models.GoogLeNet_Weights
            :members:
            :noindex:
        """
        weights = (GoogLeNet_QuantizedWeights if quantize else GoogLeNet_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, "init_weights", False)
            _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 = QuantizableGoogLeNet(**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))
            if not original_aux_logits:
                model.aux_logits = False
                model.aux1 = None  # type: ignore[assignment]
                model.aux2 = None  # type: ignore[assignment]
            else:
                warnings.warn(
                    "auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train them"
                )

        return model

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