+ jF ^RIHt^RIHtHtHt^RIt^RIHt^RIH t ^RI H t H t H t HtHtHtHt^RIHt^RIHtHtHt^RIHt^R IHtHt^R IHtHtH t .R;Ot!!RR] 4t"!RR] 4t#!RR ] 4t$RRlt%RR R#)zFuse conv/bn/relu modules in resnet models Fuse conv+bn+relu/ Conv+relu/conv+Bn modules to prepare for quantization. Model is modified in place. Note that this operation does not change numerics and the model after modification is in floating point TrPNrT)rmodulestypercr rW)r8rLms&& r*rWQuantizableResNet.fuse_modelrsH d4fdKAAw//47>S3S V$ r-)r}r{r/rYras@@r*rrbs(;;  % % %r-cV^8dQhR\\\\3,,R\\ ,R\ \,R\R\R\R\/#)r#blocklayersweightsprogressquantizer%r&) rrr rclistintrrrNrr)r(s"r*r+r+sh +-BBC D Ik "     r-c ~VeW\VR\VPR,44RVP9d\VRVPR,4VPRR4p\ W3/VBp\ V4V'd \ Wv4Ve#VPVPVRR74V#)N num_classes categoriesbackendfbgemmT)r check_hash) rlenmetapoprrrload_state_dictget_state_dict)rrrrrr%rmodels&&&&&, r*_resnetrsfmSl9S5TU  $ !&)W\\)5L MjjH-G e 6v 6E%u& g44hSW4XY Lr-min_sizerrrrecipezdhttps://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models_docsz These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized weights listed below. c|]tRt^t]!R]!]^R7/]CRRR]PRRRR R R //R R RR/CR7t ] t Rt R#)rzJhttps://download.pytorch.org/models/quantized/resnet18_fbgemm_16fa66dd.pth crop_size num_paramsi(^ unquantized_metrics ImageNet-1Kacc@1gV-_Q@acc@5g r8V@_opsg/$? _file_sizeg`"y&@url transformsrN) rZr[r\r]rrr _COMMON_METAr IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr^rr-r*rrsk" X.#>    ( +99 VV  E &  "#Gr-c]tRt^t]!R]!]^R7/]CRRR]PRRRR R R //R R RR/CR7t ]!R]!]^^R7/]CRRR]PRRRRR R//R R RR/CR7t ] t RtR#)rzJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pthrri(rrrrg{GR@rgjt4W@rgB`"[@rgM8@rzJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm-23753f79.pthr resize_sizeg5^I T@gX9vW@g8@rN)rZr[r\r]rrrrr rr IMAGENET1K_V2IMAGENET1K_FBGEMM_V2rr^rr-r*rrs" X.#>    ( +99 VV  E &  "# X.#3O    ( +99 VV  E &  "#Gr-c]tRt^t]!R]!]^R7/]CRRR]PRRRR R R //R R RR/CR7t ]!R]!]^^R7/]CRRR]PRRRRR R//R R RR/CR7t ] t RtR#)rzQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pthrri(JrrrrgvS@rgQW@rgDli0@rgV-U@rzQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm-ee16d00c.pthrg~jT@g rX@gzGU@rN)rZr[r\r]rrrrrrrrrrr^rr-r*rrs" _.#>    ( 3AA VV  F &  "# _.#3O    ( 3AA VV  F &  "#Gr-c]tRtRt]!R]!]^^R7/]CRRRRR]PR R R R R R//RRRR/CR7t ] t Rt R#)rizRhttps://download.pytorch.org/models/quantized/resnext101_64x4d_fbgemm-605a1cb3.pthrri(mrz+https://github.com/pytorch/vision/pull/5935rrrrgxT@rg/X@rgQ.@rg$cT@rrN) rZr[r\r]rrrrrrrrr^rr-r*rrss" `.#3O    ( C 3AA VV  E &  $#Gr-quantized_resnet18)name pretrainedcrVPRR4'd\P#\P#rF)getrrr rr%s&r*r1zz*e,, & : : 0!// 0r-)rrrTrFc V^8dQhR\\\\3,,R\R\R\ R\ /#r#rrrr%r&)rrrr rNrr)r(s"r*r+r+&T&_&_ e57GGH I&_&_ &_  &_  &_r-c rV'd\M\PV4p\\.ROWV3/VB#)aCResNet-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: )r#r#r#r#)rr verifyrr rrrr%s$$$,r*resnet18r4^-5(:JRRSZ[G (,8 ^W] ^^r-quantized_resnet50crVPRR4'd\P#\P#r)rrrr rrs&r*rrSrr-c V^8dQhR\\\\3,,R\R\R\ R\ /#r)rrrr rNrr)r(s"r*r+r+Zrr-c rV'd\M\PV4p\\.ROWV3/VB#)aCResNet-50 model from `Deep Residual Learning for Image Recognition `_ .. note:: Note that ``quantize = True`` returns a quantized model with 8 bit weights. Quantized models only support inference and run on CPUs. GPU inference is not yet supported. Args: weights (:class:`~torchvision.models.quantization.ResNet50_QuantizedWeights` or :class:`~torchvision.models.ResNet50_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.ResNet50_QuantizedWeights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. quantize (bool, optional): If True, return a quantized version of the model. Default is False. **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.ResNet50_QuantizedWeights :members: .. autoclass:: torchvision.models.ResNet50_Weights :members: :noindex: )r)rr rrrcrs$$$,r*resnet50rOrr-quantized_resnext101_32x8dcrVPRR4'd\P#\P#r)rrrrrrs&r*rr1zz*e,, . B B 8)77 8r-c V^8dQhR\\\\3,,R\R\R\ R\ /#r)rrrrrNrr)r(s"r*r+r+T(`(` e=?WWX Y(`(` (`  (`  (`r-c V'd\M\PV4p\VR^ 4\VR^4\ \ .ROWV3/VB#)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: groupswidth_per_grouprrr)rrrrrrcrs$$$,r*resnext101_32x8drM^5=0BZbbcjkG&(B/&"3Q7 (-H _X^ __r-quantized_resnext101_64x4dcrVPRR4'd\P#\P#r)rrrrrrs&r*rrrr-c V^8dQhR\\\\3,,R\R\R\ R\ /#r)rrrrrNrr)r(s"r*r+r+rr-c V'd\M\PV4p\VR^@4\VR^4\ \ .ROWV3/VB#)aResNeXt-101 64x4d model from `Aggregated Residual Transformation for Deep Neural Networks `_ .. note:: Note that ``quantize = True`` returns a quantized model with 8 bit weights. Quantized models only support inference and run on CPUs. GPU inference is not yet supported. Args: weights (:class:`~torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights` or :class:`~torchvision.models.ResNeXt101_64X4D_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.ResNet101_64X4D_QuantizedWeights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. quantize (bool, optional): If True, return a quantized version of the model. Default is False. **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights :members: .. autoclass:: torchvision.models.ResNeXt101_64X4D_Weights :members: :noindex: rrr)rrrrrrcrs$$$,r*resnext101_64x4drrr-) rrrrrrrrr)r)/ functoolsrtypingrrrr2torch.nnr3rtorchvision.models.resnetr r r r r rrtransforms._presetsr_apirrr_metar_utilsrrutilsrrr__all__r rcrrrrrrrrrrrrr-r*rs"'' 777(C?? MJM8MJMB%%:4& x t   # #*## ##L## ##L# #,)*   &_LP&_&_ &_ +&_R)*   &_LP&_&_ &_ +&_R12   (`\`(`(` (` 3(`V12   (`\`(`(` (` 3(`r-