!EiXSSKJr SSKrSSKJr SSKJrJr SSKJ r J r J r SSK J r SSKJr SS KJr /S Qr"S S \5r"S S\5r"SS\\5r"SS\5r"SS\\5r"SS\5r"SS\\5r"SS\5r"SS\\5r"SS\5r g))AnyN)Tensor) functionalinit) ParameterUninitializedBufferUninitializedParameter) SyncBatchNorm)LazyModuleMixin)Module) BatchNorm1dLazyBatchNorm1d BatchNorm2dLazyBatchNorm2d BatchNorm3dLazyBatchNorm3dr c ^\rSrSr%SrSr/SQr\\S'\ \S'\ S-\S'\ \S '\ \S 'SS\S\ S\ S-S \ S \ S S4 U4S jjjr SS jr SSjr SrSrSU4SjjrSrU=r$) _NormBasez,Common base of _InstanceNorm and _BatchNorm.)track_running_statsmomentumeps num_featuresaffinerrNrrrreturnc >XgS.n[T U]5 XlX lX0lX@lXPlUR (aK[[R"U40UD65Ul [[R"U40UD65Ul O$URSS5 URSS5 UR (aURS[R"U40UD65 URS[R"U40UD65 U U URS[R "SS[R"0UR%5V V s0sHupU S:wdM X_M sn n D65 U O6URSS5 URSS5 URSS5 UR'5 gs sn n f) Ndevicedtypeweightbias running_mean running_varnum_batches_trackedr!r)super__init__rrrrrrtorchemptyr"r#register_parameterregister_bufferzerosonestensorlongitemsreset_parameters) selfrrrrrr r!factory_kwargskv __class__s W/home/mande/repo/quber/.venv/lib/python3.13/site-packages/torch/nn/modules/batchnorm.pyr)_NormBase.__init__&s%+; (  #6 ;;#EKK $O$OPDK!%++l"Mn"MNDI  # #Hd 3  # #FD 1  # #   L KN K   uzz,I.I     % **)7(<(<(>O(>!w,tqt(>O      6   5  !6 = Ps  G'GcUR(aPURR5 URR S5 UR R5 gg)Nr )rr$zero_r%fill_r&r4s r9reset_running_stats_NormBase.reset_running_statsVsJ  # #    # # %    " "1 %  $ $ * * , $cUR5 UR(aA[R"UR5 [R "UR 5 ggN)r?rrones_r"zeros_r#r>s r9r3_NormBase.reset_parameters^s:   " ;; JJt{{ # KK " rAc[erC)NotImplementedErrorr4inputs r9_check_input_dim_NormBase._check_input_dimds!!rAc:SR"S0URD6$)Nzj{num_features}, eps={eps}, momentum={momentum}, affine={affine}, track_running_stats={track_running_stats})format__dict__r>s r9 extra_repr_NormBase.extra_reprgs) 88> ? PAE P rAc t>URSS5nUbUS:aUR(avUS-n X;alURb:URR[R"S5:wa URO"[R "S[R S9X'[T U]!UUUUUUU5 g)Nversionrr&metar)r!) getrr&r r*r0r1r(_load_from_state_dict) r4 state_dictprefixlocal_metadatastrict missing_keysunexpected_keys error_msgsrTnum_batches_tracked_keyr8s r9rW_NormBase._load_from_state_dictms!$$Y5 Ow{0H0H'-/D&D #&8//;00775<<;OO,,auzz: 3 %        rA)rr#rrrrr"h㈵>皙?TTNNrN)__name__ __module__ __qualname____firstlineno____doc___version __constants__int__annotations__floatboolr)r?r3rKrQrW__static_attributes__ __classcell__r8s@r9rrs6HXM Jdl L!$$(. . . $, .  . " .  . . `-# "      rArc l^\rSrSrS S\S\S\S-S\S\SS4 U4S jjjrS \S\4S jr S r U=r $) _BatchNormNrrrrrrc4>XgS.n[T U]"XX4U40UD6 gNr)r(r)) r4rrrrrr r!r5r8s r9r)_BatchNorm.__init__s*%+;  x1D HV rArJc URU5 URcSnO URnUR(akUR(aZURbMURR S5 URcS[ UR5- nO URnUR(aSnO#URSL=(a URSLn[R"UUR(aUR(a UROSUR(aUR(a UROSURURUUUR5$)Nr ?T)rKrtrainingrr&add_rnr$r%F batch_normr"r#r)r4rJexponential_average_factor bn_trainings r9forward_BatchNorm.forwards' e$ == ), &)- & ==T55''3((--a0==(14uT=U=U7V1V.15.  ==K,,4T4;K;Kt;SK || }}(@(@!!$(MMT5M5MD  SW KK II  & HH  rArNra) rerfrgrhrlrnror)rrrprqrrs@r9rtrtsw!$$(      $,    "      0 V0 0 0 rArtcn^\rSrSr%\\S'\\S'SS U4SjjjrS U4SjjrS SjrSr U=r $) _LazyNormBaser"r#c >XVS.n[T U]"SUUSS40UD6 X0lX@lUR(a [ S0UD6Ul[ S0UD6UlUR(ax[S0UD6Ul[S0UD6Ul [R"SS[R0UR5VV s0sHupUS:wdM X_M sn nD6Ulggs sn nf)NrrFr!rNr')r(r)rrr r"r#rr$r%r*r0r1r2r&) r4rrrrr r!r5r6r7r8s r9r)_LazyNormBase.__init__s%+;         #6 ;;0B>BDK.@@DI  # # 3 En ED 2D^DD ',||(jj(%3$8$8$:K$:DAa7l414$:K (D $ $Ls ? C"C"cp>UR5(d URS:wa[TU] 5 ggg)Nr)has_uninitialized_paramsrr(r3)r4r8s r9r3_LazyNormBase.reset_parameterss3,,..43D3D3I G $ &4J.rAcUR5(Ga3URSUlUR(a[ UR [ 5(d [S5e[ UR[ 5(d [S5eUR RUR45 URRUR45 UR(aLURRUR45 URRUR45 UR5 gg)Nr z-self.weight must be an UninitializedParameterz+self.bias must be an UninitializedParameter)rshaperr isinstancer"r AssertionErrorr# materializerr$r%r3rIs r9initialize_parameters#_LazyNormBase.initialize_parameterss  ( ( * * % AD {{!$++/EFF(G"$))-CDD()VWW ''):):(<= %%t'8'8&:;''!!--&&(  ,,&&(  ! ! #% +rA)rr#r&rr$r%rr"rard) rerfrgrhr rmr)r3rrprqrrs@r9rrsG ""    & &&P' $$rArc"\rSrSrSrSSjrSrg)ria Applies Batch Normalization over a 2D or 3D input. Method described in the paper `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift `__ . .. math:: y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta The mean and standard-deviation are calculated per-dimension over the mini-batches and :math:`\gamma` and :math:`\beta` are learnable parameter vectors of size `C` (where `C` is the number of features or channels of the input). By default, the elements of :math:`\gamma` are set to 1 and the elements of :math:`\beta` are set to 0. At train time in the forward pass, the variance is calculated via the biased estimator, equivalent to ``torch.var(input, correction=0)``. However, the value stored in the moving average of the variance is calculated via the unbiased estimator, equivalent to ``torch.var(input, correction=1)``. Also by default, during training this layer keeps running estimates of its computed mean and variance, which are then used for normalization during evaluation. The running estimates are kept with a default :attr:`momentum` of 0.1. If :attr:`track_running_stats` is set to ``False``, this layer then does not keep running estimates, and batch statistics are instead used during evaluation time as well. .. note:: This :attr:`momentum` argument is different from one used in optimizer classes and the conventional notion of momentum. Mathematically, the update rule for running statistics here is :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the new observed value. Because the Batch Normalization is done over the `C` dimension, computing statistics on `(N, L)` slices, it's common terminology to call this Temporal Batch Normalization. Args: num_features: number of features or channels :math:`C` of the input eps: a value added to the denominator for numerical stability. Default: 1e-5 momentum: the value used for the running_mean and running_var computation. Can be set to ``None`` for cumulative moving average (i.e. simple average). Default: 0.1 affine: a boolean value that when set to ``True``, this module has learnable affine parameters. Default: ``True`` track_running_stats: a boolean value that when set to ``True``, this module tracks the running mean and variance, and when set to ``False``, this module does not track such statistics, and initializes statistics buffers :attr:`running_mean` and :attr:`running_var` as ``None``. When these buffers are ``None``, this module always uses batch statistics. in both training and eval modes. Default: ``True`` Shape: - Input: :math:`(N, C)` or :math:`(N, C, L)`, where :math:`N` is the batch size, :math:`C` is the number of features or channels, and :math:`L` is the sequence length - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input) Examples:: >>> # With Learnable Parameters >>> m = nn.BatchNorm1d(100) >>> # Without Learnable Parameters >>> m = nn.BatchNorm1d(100, affine=False) >>> input = torch.randn(20, 100) >>> output = m(input) NcUR5S:wa2UR5S:wa[SUR5S35eggNrzexpected 2D or 3D input (got D input)dim ValueErrorrIs r9rKBatchNorm1d._check_input_dimb@ 99;!  q 0`__ . .. math:: y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta The mean and standard-deviation are calculated per-dimension over the mini-batches and :math:`\gamma` and :math:`\beta` are learnable parameter vectors of size `C` (where `C` is the input size). By default, the elements of :math:`\gamma` are set to 1 and the elements of :math:`\beta` are set to 0. At train time in the forward pass, the standard-deviation is calculated via the biased estimator, equivalent to ``torch.var(input, correction=0)``. However, the value stored in the moving average of the standard-deviation is calculated via the unbiased estimator, equivalent to ``torch.var(input, correction=1)``. Also by default, during training this layer keeps running estimates of its computed mean and variance, which are then used for normalization during evaluation. The running estimates are kept with a default :attr:`momentum` of 0.1. If :attr:`track_running_stats` is set to ``False``, this layer then does not keep running estimates, and batch statistics are instead used during evaluation time as well. .. note:: This :attr:`momentum` argument is different from one used in optimizer classes and the conventional notion of momentum. Mathematically, the update rule for running statistics here is :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the new observed value. Because the Batch Normalization is done over the `C` dimension, computing statistics on `(N, H, W)` slices, it's common terminology to call this Spatial Batch Normalization. Args: num_features: :math:`C` from an expected input of size :math:`(N, C, H, W)` eps: a value added to the denominator for numerical stability. Default: 1e-5 momentum: the value used for the running_mean and running_var computation. Can be set to ``None`` for cumulative moving average (i.e. simple average). Default: 0.1 affine: a boolean value that when set to ``True``, this module has learnable affine parameters. Default: ``True`` track_running_stats: a boolean value that when set to ``True``, this module tracks the running mean and variance, and when set to ``False``, this module does not track such statistics, and initializes statistics buffers :attr:`running_mean` and :attr:`running_var` as ``None``. When these buffers are ``None``, this module always uses batch statistics. in both training and eval modes. Default: ``True`` Shape: - Input: :math:`(N, C, H, W)` - Output: :math:`(N, C, H, W)` (same shape as input) Examples:: >>> # With Learnable Parameters >>> m = nn.BatchNorm2d(100) >>> # Without Learnable Parameters >>> m = nn.BatchNorm2d(100, affine=False) >>> input = torch.randn(20, 100, 35, 45) >>> output = m(input) NcfUR5S:wa[SUR5S35egNzexpected 4D input (got rrrIs r9rKBatchNorm2d._check_input_dim0 99;! 6uyy{m8LM M rArNrdrrNrAr9rrENNrArc&\rSrSrSr\rSSjrSrg)riaA :class:`torch.nn.BatchNorm2d` module with lazy initialization. Lazy initialization is done for the ``num_features`` argument of the :class:`BatchNorm2d` that is inferred from the ``input.size(1)``. The attributes that will be lazily initialized are `weight`, `bias`, `running_mean` and `running_var`. Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation on lazy modules and their limitations. Args: eps: a value added to the denominator for numerical stability. Default: 1e-5 momentum: the value used for the running_mean and running_var computation. Can be set to ``None`` for cumulative moving average (i.e. simple average). Default: 0.1 affine: a boolean value that when set to ``True``, this module has learnable affine parameters. Default: ``True`` track_running_stats: a boolean value that when set to ``True``, this module tracks the running mean and variance, and when set to ``False``, this module does not track such statistics, and initializes statistics buffers :attr:`running_mean` and :attr:`running_var` as ``None``. When these buffers are ``None``, this module always uses batch statistics. in both training and eval modes. Default: ``True`` NcfUR5S:wa[SUR5S35egrrrIs r9rK LazyBatchNorm2d._check_input_dimrrArNrd) rerfrgrhrirrrKrprNrAr9rr4 MNrArc"\rSrSrSrSSjrSrg)ria Applies Batch Normalization over a 5D input. 5D is a mini-batch of 3D inputs with additional channel dimension as described in the paper `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift `__ . .. math:: y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta The mean and standard-deviation are calculated per-dimension over the mini-batches and :math:`\gamma` and :math:`\beta` are learnable parameter vectors of size `C` (where `C` is the input size). By default, the elements of :math:`\gamma` are set to 1 and the elements of :math:`\beta` are set to 0. At train time in the forward pass, the standard-deviation is calculated via the biased estimator, equivalent to ``torch.var(input, correction=0)``. However, the value stored in the moving average of the standard-deviation is calculated via the unbiased estimator, equivalent to ``torch.var(input, correction=1)``. Also by default, during training this layer keeps running estimates of its computed mean and variance, which are then used for normalization during evaluation. The running estimates are kept with a default :attr:`momentum` of 0.1. If :attr:`track_running_stats` is set to ``False``, this layer then does not keep running estimates, and batch statistics are instead used during evaluation time as well. .. note:: This :attr:`momentum` argument is different from one used in optimizer classes and the conventional notion of momentum. Mathematically, the update rule for running statistics here is :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the new observed value. Because the Batch Normalization is done over the `C` dimension, computing statistics on `(N, D, H, W)` slices, it's common terminology to call this Volumetric Batch Normalization or Spatio-temporal Batch Normalization. Args: num_features: :math:`C` from an expected input of size :math:`(N, C, D, H, W)` eps: a value added to the denominator for numerical stability. Default: 1e-5 momentum: the value used for the running_mean and running_var computation. Can be set to ``None`` for cumulative moving average (i.e. simple average). Default: 0.1 affine: a boolean value that when set to ``True``, this module has learnable affine parameters. Default: ``True`` track_running_stats: a boolean value that when set to ``True``, this module tracks the running mean and variance, and when set to ``False``, this module does not track such statistics, and initializes statistics buffers :attr:`running_mean` and :attr:`running_var` as ``None``. When these buffers are ``None``, this module always uses batch statistics. in both training and eval modes. Default: ``True`` Shape: - Input: :math:`(N, C, D, H, W)` - Output: :math:`(N, C, D, H, W)` (same shape as input) Examples:: >>> # With Learnable Parameters >>> m = nn.BatchNorm3d(100) >>> # Without Learnable Parameters >>> m = nn.BatchNorm3d(100, affine=False) >>> input = torch.randn(20, 100, 35, 45, 10) >>> output = m(input) NcfUR5S:wa[SUR5S35egNzexpected 5D input (got rrrIs r9rKBatchNorm3d._check_input_dim@rrArNrdrrNrAr9rrrrArc&\rSrSrSr\rSSjrSrg)riEaA :class:`torch.nn.BatchNorm3d` module with lazy initialization. Lazy initialization is done for the ``num_features`` argument of the :class:`BatchNorm3d` that is inferred from the ``input.size(1)``. The attributes that will be lazily initialized are `weight`, `bias`, `running_mean` and `running_var`. Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation on lazy modules and their limitations. Args: eps: a value added to the denominator for numerical stability. Default: 1e-5 momentum: the value used for the running_mean and running_var computation. Can be set to ``None`` for cumulative moving average (i.e. simple average). Default: 0.1 affine: a boolean value that when set to ``True``, this module has learnable affine parameters. Default: ``True`` track_running_stats: a boolean value that when set to ``True``, this module tracks the running mean and variance, and when set to ``False``, this module does not track such statistics, and initializes statistics buffers :attr:`running_mean` and :attr:`running_var` as ``None``. When these buffers are ``None``, this module always uses batch statistics. in both training and eval modes. Default: ``True`` NcfUR5S:wa[SUR5S35egrrrIs r9rK LazyBatchNorm3d._check_input_dimbrrArNrd) rerfrgrhrirrrKrprNrAr9rrErrArc^\rSrSrSrSS\S\S\S-S\S\S \S-S S4U4S jjjr SS jr SS jr S\ S \ 4Sjr \SSj5rSrU=r$)r igaApplies Batch Normalization over a N-Dimensional input. The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift `__ . .. math:: y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta The mean and standard-deviation are calculated per-dimension over all mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` are learnable parameter vectors of size `C` (where `C` is the input size). By default, the elements of :math:`\gamma` are sampled from :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. The standard-deviation is calculated via the biased estimator, equivalent to `torch.var(input, correction=0)`. Also by default, during training this layer keeps running estimates of its computed mean and variance, which are then used for normalization during evaluation. The running estimates are kept with a default :attr:`momentum` of 0.1. If :attr:`track_running_stats` is set to ``False``, this layer then does not keep running estimates, and batch statistics are instead used during evaluation time as well. .. note:: This :attr:`momentum` argument is different from one used in optimizer classes and the conventional notion of momentum. Mathematically, the update rule for running statistics here is :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the new observed value. Because the Batch Normalization is done for each channel in the ``C`` dimension, computing statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch Normalization or Spatio-temporal Batch Normalization. Currently :class:`SyncBatchNorm` only supports :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping Network with DDP. Args: num_features: :math:`C` from an expected input of size :math:`(N, C, +)` eps: a value added to the denominator for numerical stability. Default: ``1e-5`` momentum: the value used for the running_mean and running_var computation. Can be set to ``None`` for cumulative moving average (i.e. simple average). Default: 0.1 affine: a boolean value that when set to ``True``, this module has learnable affine parameters. Default: ``True`` track_running_stats: a boolean value that when set to ``True``, this module tracks the running mean and variance, and when set to ``False``, this module does not track such statistics, and initializes statistics buffers :attr:`running_mean` and :attr:`running_var` as ``None``. When these buffers are ``None``, this module always uses batch statistics. in both training and eval modes. Default: ``True`` process_group: synchronization of stats happen within each process group individually. Default behavior is synchronization across the whole world Shape: - Input: :math:`(N, C, +)` - Output: :math:`(N, C, +)` (same shape as input) .. note:: Synchronization of batchnorm statistics occurs only while training, i.e. synchronization is disabled when ``model.eval()`` is set or if ``self.training`` is otherwise ``False``. Examples:: >>> # xdoctest: +SKIP >>> # With Learnable Parameters >>> m = nn.SyncBatchNorm(100) >>> # creating process group (optional) >>> # ranks is a list of int identifying rank ids. >>> ranks = list(range(8)) >>> r1, r2 = ranks[:4], ranks[4:] >>> # Note: every rank calls into new_group for every >>> # process group created, even if that rank is not >>> # part of the group. >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] >>> # Without Learnable Parameters >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) >>> input = torch.randn(20, 100, 35, 45, 10) >>> output = m(input) >>> # network is nn.BatchNorm layer >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) >>> # only single gpu per process is currently supported >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( >>> sync_bn_network, >>> device_ids=[args.local_rank], >>> output_device=args.local_rank) Nrrrrr process_grouprc @>XxS.n [T U]"XX4U40U D6 X`lgrw)r(r)r) r4rrrrrrr r!r5r8s r9r)SyncBatchNorm.__init__s2%+;  x1D HV +rAcfUR5S:a[SUR5S35eg)Nrz expected at least 2D input (got rrrIs r9rKSyncBatchNorm._check_input_dims/ 99;?? }HUV V rAcDURS5S:Xa [S5eg)Nr rz9SyncBatchNorm number of input channels should be non-zero)sizerrIs r9_check_non_zero_input_channels,SyncBatchNorm._check_non_zero_input_channelss' ::a=A K  rArJc FURU5 URU5 URcSnO URnUR(a{UR(ajUR c [ S5eUR RS5 URcSUR R5- nO URnUR(aSnO#URSL=(a URSLnUR(aUR(a UROSnUR(aUR(a UROSnU=(aV UR=(aC [RR5=(a [RR5nU(aURR SSS [R"R%54;a*['S [R"R%535e[RR(R*nUR,(a UR,n[RR/U5nUS:nU(d;[0R2"UUUUR4UR6UUUR85$U(d [ S 5e[:R<"UUR4UR6UUUR8UWW5 $) z Runs the forward pass. Nrzz$num_batches_tracked must not be Noner r{Tcudahpuxpuz;SyncBatchNorm expected input tensor to be on GPU or XPU or zbn_training must be True)rKrrr|rr&rr}itemr$r%r* distributed is_availableis_initializedr type_C_get_privateuse1_backend_namergroupWORLDrget_world_sizer~rr"r#rsync_batch_normapply) r4rJrrr$r% need_syncr world_sizes r9rSyncBatchNorm.forwards e$ ++E2 == ), &)- & ==T55''/$%KLL  $ $ ) )! ,}}$-043K3K3P3P3R-R*-1]]*  ==K,,4T4;K;Kt;SK &*]]d6N6ND  TX %)MMT5M5MD  SW   3  3!!..0 3!!002  ||  668 ) !Qxx==?@B "--3399M!! $ 2 2 **99-HJ"QI<<  *  $%?@@"((  *  rAc6Un[U[RRRR 5(Ga [RR URURURURURU5nUR(a@[R"5 URUl URUlSSS5 URUlUR UlUR"UlUR$Ul['US5(aUR(UlUR+5H%upEUR-X@R/XR55 M' AU$!,(df  N=f)aConverts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. Args: module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers process_group (optional): process group to scope synchronization, default is the whole world Returns: The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, a new :class:`torch.nn.SyncBatchNorm` layer object will be returned instead. Example:: >>> # Network with nn.BatchNorm layer >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) >>> module = torch.nn.Sequential( >>> torch.nn.Linear(20, 100), >>> torch.nn.BatchNorm1d(100), >>> ).cuda() >>> # creating process group (optional) >>> # ranks is a list of int identifying rank ids. >>> ranks = list(range(8)) >>> r1, r2 = ranks[:4], ranks[4:] >>> # Note: every rank calls into new_group for every >>> # process group created, even if that rank is not >>> # part of the group. >>> # xdoctest: +SKIP("distributed") >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) Nqconfig)rr*nnmodules batchnormrtr rrrrrno_gradr"r#r$r%r&r|hasattrrnamed_children add_moduleconvert_sync_batchnorm)clsmoduler module_outputnamechilds r9r$SyncBatchNorm.convert_sync_batchnormLs/H fehh..88CC D D!HH22##  ** M}}]]_+1==M()/M&%*0)<)rs *UU8! t t n@ @ FE$OYE$PIT*ITXTmZTDJN*JNZNmZNDJN*JNZNmZNDbJbrA