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Note that this operation does not change numerics and the model after modification is in floating point rETrFN)rmodulestyperRr'rK)r4rDmr7r7r8rKrs   zQuantizableResNet.fuse_modelr+rLr7r7r5r8rbs rblocklayersweightsprogressquantizer)r*cKs|durt|dt|jdd|jvrt|d|jd|dd}t||fi|}t||r7t|||durE||j|dd|S)NZ num_classes categoriesbackendfbgemmT)r`Z check_hash) rlenmetapoprrrZload_state_dictZget_state_dict)r]r^r_r`rar)rcmodelr7r7r8_resnets   ri)rrrdzdhttps://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-modelsz These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized weights listed below. )Zmin_sizerbrcrecipeZ_docsc @sFeZdZedeeddiedejddddid d d d Z e Z d S)rzJhttps://download.pytorch.org/models/quantized/resnet18_fbgemm_16fa66dd.pth crop_sizei(^ ImageNet-1KgV-_Q@g r8V@zacc@1zacc@5g/$?g`"y&@ num_params unquantized_metrics_ops _file_sizeurlZ transformsrfN) rMrNrOrrr _COMMON_METAr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr7r7r7r8rs& rc @~eZdZedeeddiedejddddid d d d Z ed eedddiedej ddddid dd d Z e Z dS)rzJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pthrkrli(rng{GR@gjt4W@rogB`"[@gM8@rprvzJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm-23753f79.pthrmZ resize_sizeg5^I T@gX9vW@g8@N) rMrNrOrrrrxr ryrz IMAGENET1K_V2IMAGENET1K_FBGEMM_V2r{r7r7r7r8rH  rc @r|)r zQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pthrkrli(JrngvS@gQW@rogDli0@gV-U@rprvzQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm-ee16d00c.pthr}r~g~jT@g rX@gzGU@N) rMrNrOrrrrxrryrzrrr{r7r7r7r8r rr c @sJeZdZedeedddieddejddd d id d d dZ e Z dS)r!zRhttps://download.pytorch.org/models/quantized/resnext101_64x4d_fbgemm-605a1cb3.pthrkr}r~i(mz+https://github.com/pytorch/vision/pull/5935rngxT@g/X@rogQ.@g$cT@)rqrjrrrsrtrurvN) rMrNrOrrrrxrryrzr{r7r7r7r8r!s( r!Zquantized_resnet18)nameZ pretrainedcC|ddr tjStjSNraF)getrrzr ryr)r7r7r8 r)r_TF)r_r`racK.|rtnt|}ttgd|||fi|S)aResNet-18 model from `Deep Residual Learning for Image Recognition `_ .. 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 `_ for more details about this class. .. autoclass:: torchvision.models.quantization.ResNet18_QuantizedWeights :members: .. autoclass:: torchvision.models.ResNet18_Weights :members: :noindex: )rrrr)rr verifyrir'r_r`rar)r7r7r8r"-r"Zquantized_resnet50cCrr)rrrzr ryrr7r7r8rQrcKr)aResNet-50 model from `Deep Residual Learning for Image Recognition `_ .. 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 `_ for more details about this class. .. autoclass:: torchvision.models.quantization.ResNet50_QuantizedWeights :members: .. autoclass:: torchvision.models.ResNet50_Weights :members: :noindex: )rr)rr rrirRrr7r7r8r#Mrr#Zquantized_resnext101_32x8dcCrr)rr rzrryrr7r7r8rrcKF|rtnt|}t|ddt|ddttgd|||fi|S)aResNeXt-101 32x8d model from `Aggregated Residual Transformation for Deep Neural Networks `_ .. 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 `_ for more details about this class. .. autoclass:: torchvision.models.quantization.ResNeXt101_32X8D_QuantizedWeights :members: .. autoclass:: torchvision.models.ResNeXt101_32X8D_Weights :members: :noindex: groups width_per_grouprrr)r rrrrirRrr7r7r8r$-  r$Zquantized_resnext101_64x4dcCrr)rr!rzrryrr7r7r8rrcKr)aResNeXt-101 64x4d model from `Aggregated Residual Transformation for Deep Neural Networks `_ .. 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 `_ for more details about this class. .. autoclass:: torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights :members: .. autoclass:: torchvision.models.ResNeXt101_64X4D_Weights :members: :noindex: r@rrr)r!rrrrirRrr7r7r8r%rr%)3 functoolsrtypingrrrrrr.Ztorch.nnr/rZtorchvision.models.resnetr r r r r rrZtransforms._presetsrZ_apirrr_metar_utilsrrutilsrrr__all__r'rRrintrPrirxrrr r!r"r#r$r%r7r7r7r8s   $   !  && ) ) +