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__ = [

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 =
        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] = nn.quantized.FloatFunctional()

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

class QuantizableInceptionAux(InceptionAux):
    # TODO
    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
    def __init__(self, *args: Any, **kwargs: Any) -> None:
        super().__init__(  # type: ignore[misc]
            *args, blocks=[QuantizableBasicConv2d, QuantizableInception, QuantizableInceptionAux], **kwargs
        self.quant =
        self.dequant =

    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 = 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)
            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:

[docs]class GoogLeNet_QuantizedWeights(WeightsEnum): IMAGENET1K_FBGEMM_V1 = Weights( url="", transforms=partial(ImageClassification, crop_size=224), meta={ "num_params": 6624904, "min_size": (15, 15), "categories": _IMAGENET_CATEGORIES, "backend": "fbgemm", "recipe": "", "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
[docs]@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 <>`__. .. 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 <>`_ 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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